Hierarchical Quick Shift (HQuickSHIFT)¶
Export the external libraries first. We also define the plotting and random initialization functions and global variables.
In [13]:
%reload_ext autoreload
%autoreload 2
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import glob
import os
import sys
import seaborn as sns
import sklearn
from sklearn.utils import shuffle
from sklearn.preprocessing import StandardScaler
import sklearn.datasets as data
import sklearn.cluster as cluster
import hdbscan
from QuickshiftPP import *
from MedoidShift_and_QuickShift.quick_shift_ import QuickShift
import networkx as nx
from mlpack import emst
from mlpack import fastmks
import umap
import torch
import fastai
from fastai.utils import *
from fastai.core import *
from fastai.text import *
USE_GPU = True
dtype = torch.float32
use_cuda = torch.cuda.is_available()
if USE_GPU and use_cuda:
device = torch.device('cuda')
else:
device = torch.device('cpu')
print('using device:', device)
# For reproducibility: origin of the randomness during training
seed = 1234
seed_lst = [0, 23, 42, 1234, 43210, 1133557799, 22446688, 123456789, 987654321, 86420]
def random_seed(seed_value, use_cuda):
np.random.seed(seed_value) # cpu vars
torch.manual_seed(seed_value) # cpu vars
random.seed(seed_value) # Python
if use_cuda:
torch.cuda.manual_seed(seed_value)
torch.cuda.manual_seed_all(seed_value) # gpu vars
torch.backends.cudnn.deterministic = True #needed
torch.backends.cudnn.benchmark = False
random_seed(seed_value=seed, use_cuda=use_cuda)
# suppress scientific float notation
np.set_printoptions(precision=6, suppress=True)
# filter out informational none critical warnings
import warnings
# matplotlib warnings are subclass of UserWarning
warnings.simplefilter("ignore", category=UserWarning)
fig_size = (20, 16)
plot_kwds = {'alpha' : 1, 's' : 40, 'linewidths':0}
def plot_clusters(data, labels=None, algorithm=None, args=None, kwds=None, plt_title=None, file_name=None):
plt.figure(figsize=fig_size)
if algorithm is not None:
if algorithm.__name__=='QuickshiftPP':
model = algorithm(**kwds)
model.fit(data)
labels = model.memberships
else:
labels = algorithm(*args, **kwds).fit_predict(data)
max_n_labels = np.maximum(8, np.unique(labels).max() + 1)
# import pdb;pdb.set_trace()
palette = sns.hls_palette(max_n_labels, l=.3, s=.8)
node_color = [palette[x] if x >= 0 else (0.0, 0.0, 0.0) for x in labels]
# plt.title('Clusters found by {} and the number of Clusters: {:d}'.format(\
# str(algorithm.__name__), np.unique(labels).max() + 1), fontsize=28)
plt.setp(plt.gca().get_xticklabels(), visible=False)
plt.setp(plt.gca().get_yticklabels(), visible=False)
plt.tight_layout()
else:
if labels is not None:
max_n_labels = np.maximum(8, np.unique(labels).max() + 1)
palette = sns.hls_palette(max_n_labels, l=.3, s=.8)
node_color = [palette[x] if x >= 0 else (0.0, 0.0, 0.0) for x in labels]
else:
node_color = ['b']
plt.scatter(data.T[0], data.T[1], c=node_color, **plot_kwds)
# plt.setp(plt.gca().get_xticklabels(), visible=False)
# plt.setp(plt.gca().get_yticklabels(), visible=False)
plt.tight_layout()
# if plt_title is not None: plt.title(plt_title)
if file_name is not None: plt.savefig(file_name, dpi=200, bbox_inches='tight')
using device: cuda
It is a good habit to list the version of the libraries to reproduce the results and check their correct installation. An additional environment.yml file is also provided for this purpose under the provided folder.
You can create a new environment:
- conda env create -f environment.yml
In [14]:
print("Numpy version: %s" %(np.__version__))
print("Sklearn version: %s" %(sklearn.__version__))
print("PyTorch version: %s" %(torch.__version__))
print("Fastai version: %s" %(fastai.__version__))
print("Umap version: %s" %(umap.__version__))
Numpy version: 1.16.4 Sklearn version: 0.21.2 PyTorch version: 1.1.0 Fastai version: 1.0.46 Umap version: 0.3.9
EuclideanMinimumSpanningTree class to estimate the bandwidth in the scope of Hierarchical Quick Shift (HQuickSHIFT):
In [15]:
class EuclideanMinimumSpanningTree(object):
"""Build Euclidean MinimumSpanning Tree with MLpack's
Euclidian minimum spanning tree (emst) routine.
Parameters
----------
data: float, required
Array of input data shape (n_samples, n_features)
copy_all_inputs: bool, optional (default=True)
Params for MLpack's Euclidian minimum spanning tree (emst) and
Fast Max-Kernel Search (FastMKS).
If specified, all input parameters will be deep copied before the method
is run. This is useful for debugging problems where the input parameters
are being modified by the algorithm, but can slow down the code.
verbose: bool, optional (default=False)
Params for MLpack's Euclidian minimum spanning tree (emst).
Display informational messages and the full list of parameters and
timers at the end of execution.
leaf_size: int, optional (default=20)
Leaf size in the kd-tree built by MLpack's Euclidian minimum spanning
tree (emst) routine. One-element leaves give the empirically best
performance, but at the cost of greater memory requirements.
"""
def __init__(self, data, copy_all_inputs=True, verbose=False, leaf_size=20):
self._data = data
self._copy_all_inputs = copy_all_inputs
self._verbose = verbose
self._leaf_size = leaf_size
self._construct_graph()
def _construct_graph(self):
self._emst = emst(input=self._data, copy_all_inputs=self._copy_all_inputs,
verbose=self._verbose, leaf_size=self._leaf_size)['output']
self._bandwidth = (np.mean(self._emst[:,2]))**(1/self._data.shape[1])
def plot(self, axis=None, with_labels=False, node_size=40,
node_color='k', edge_cmap=plt.cm.viridis, edge_width=2, colorbar=True):
"""Plot the minimum spanning tree (as projected into 2D by UMAP if required).
Parameters
----------
axis: matplotlib axis, optional (default=None)
The axis to render the plot to
with_labels: bool, optional (default=False)
Set to True to draw labels on the nodes.
node_size: int, optional (default 40)
The size of nodes in the plot.
node_color: matplotlib color spec, optional (default black)
The color to render nodes.
edge_cmap: matplotlib colormap, optional (default plt.cm.viridis)
The colormap to color edges by (varying color by edge weight/distance).
Can be a cmap object.
edge_width: float, optional (default 2)
The linewidth to use for rendering edges.
colorbar: bool, optional (default True)
Whether to draw a colorbar.
Returns
-------
axis : matplotlib axis
The axis used the render the plot.
"""
self._G = nx.Graph()
for row in self._emst:
self._G.add_edge(row[0], row[1], weight=row[2])
data_dict = {index: tuple(row) for index, row in enumerate(self._data)}
nx.set_node_attributes(self._G, data_dict, 'node_pos')
if axis is None:
axis = plt.gca()
_, edge_weight = zip(*nx.get_edge_attributes(self._G,'weight').items())
node_pos = nx.get_node_attributes(self._G, 'node_pos')
edge_vmin = 0.0; edge_vmax = max(edge_weight)
nx.draw_networkx(self._G, ax=axis, pos=node_pos,
with_labels=with_labels, node_size=node_size, node_color=node_color,
width=edge_width, edge_color=edge_weight, edge_cmap=edge_cmap,
edge_vmin=edge_vmin, edge_vmax=edge_vmax)
if colorbar:
sm = plt.cm.ScalarMappable(cmap=edge_cmap,
norm=plt.Normalize(vmin=edge_vmin, vmax=edge_vmax))
sm.set_array([])
cbar = plt.colorbar(sm, ax=axis)
cbar.set_label('Euclidean distance', rotation=90)
# plt.title('HQuickSHIFT Euclidean Minimum Spanning Tree, bandwidth: {:.2f}'.format(\
# self._bandwidth), fontsize=28)
plt.setp(plt.gca().get_xticklabels(), visible=False)
plt.setp(plt.gca().get_yticklabels(), visible=False)
plt.tight_layout()
return axis
@property
def bandwidth(self):
return self._bandwidth
@property
def emst_graph(self):
return self._emst
MutualReachabilityMinimumSpanningTree class is used to determine density connected modes in the scope of Hierarchical Quick Shift (HQuickSHIFT):
In [16]:
from hdbscan.hdbscan_ import _hdbscan_boruvka_balltree, _hdbscan_boruvka_kdtree
class MutualReachabilityMinimumSpanningTree(object):
"""Build Mutual Reachability MinimumSpanning Tree with HDBSCAN
Compute the internal minimum spanning tree given a data matrix
Parameters
----------
data: float, required
Array of input data shape (n_samples, n_features)
min_samples : int, optional (default=5)
The number of samples in a neighborhood for a point (k-nn)
to be considered as a core point. This includes the point itself.
leaf_size: int, optional (default=20)
Leaf size in the ball tree built by HDBSCAN.
"""
def __init__(self, data, min_samples=5, leaf_size=20):
self._data = data
self._min_samples = min_samples
self._leaf_size = leaf_size
self._construct_graph()
def _construct_graph(self):
(_, self._mrmst) = (_hdbscan_boruvka_balltree(self._data,
min_samples=self._min_samples,
leaf_size=self._leaf_size,
gen_min_span_tree=True))
def plot(self, axis=None, with_labels=False, node_size=40,
node_color='k', edge_cmap=plt.cm.viridis, edge_width=2, colorbar=True):
"""Plot the minimum spanning tree (as projected into 2D by UMAP if required).
Parameters
----------
axis: matplotlib axis, optional (default=None)
The axis to render the plot to
with_labels: bool, optional (default=False)
Set to True to draw labels on the nodes.
node_size: int, optional (default 40)
The size of nodes in the plot.
node_color: matplotlib color spec, optional (default black)
The color to render nodes.
edge_cmap: matplotlib colormap, optional (default plt.cm.viridis)
The colormap to color edges by (varying color by edge weight/distance).
Can be a cmap object.
edge_width: float, optional (default 2)
The linewidth to use for rendering edges.
colorbar: bool, optional (default True)
Whether to draw a colorbar.
Returns
-------
axis : matplotlib axis
The axis used the render the plot.
"""
self._G = nx.Graph()
for row in self._mrmst:
self._G.add_edge(row[0], row[1], weight=row[2])
data_dict = {index: tuple(row) for index, row in enumerate(self._data)}
nx.set_node_attributes(self._G, data_dict, 'node_pos')
if axis is None:
axis = plt.gca()
_, edge_weight = zip(*nx.get_edge_attributes(self._G,'weight').items())
node_pos = nx.get_node_attributes(self._G, 'node_pos')
edge_vmin = 0.0; edge_vmax = max(edge_weight)
nx.draw_networkx(self._G, ax=axis, pos=node_pos,
with_labels=with_labels, node_size=node_size, node_color=node_color,
width=edge_width, edge_color=edge_weight, edge_cmap=edge_cmap,
edge_vmin=edge_vmin, edge_vmax=edge_vmax)
if colorbar:
sm = plt.cm.ScalarMappable(cmap=edge_cmap,
norm=plt.Normalize(vmin=edge_vmin, vmax=edge_vmax))
sm.set_array([])
cbar = plt.colorbar(sm, ax=axis)
cbar.set_label('mutual reachability distance', rotation=90)
# plt.title('Mutual Reachability Minimum Spanning Tree', fontsize=28)
plt.setp(plt.gca().get_xticklabels(), visible=False)
plt.setp(plt.gca().get_yticklabels(), visible=False)
plt.tight_layout()
return axis
@property
def mrmst_graph(self):
return self._mrmst
HQSHIFT is the main work horse and the implementation of the Hierarchical Quick Shift algorithm:
In [17]:
from sklearn.base import BaseEstimator, ClusterMixin
from hdbscan.hdbscan_ import _tree_to_labels
from hdbscan.plots import CondensedTree, SingleLinkageTree
from hdbscan._hdbscan_linkage import label
from scipy.sparse import csgraph, csr_matrix
from scipy.spatial import ConvexHull
def _find_nth_smallest(a, n):
return np.partition(a, n-1)[n-1]
def rbf_kernel(d,bw): return np.exp(-d**2/(2*bw**2))
def _extract_weight(elem): return elem[2]['weight']
# Inherits from sklearn
class HQSHIFT(BaseEstimator, ClusterMixin):
"""Perform HQSHIFT clustering from vector array.
HQSHIFT - Hierarchical Quick Shift
Performs quick shift over varying tau values and determines
the result to find a clustering that gives the best stability.
This allows HQSHIFT to find clusters of varying tau(s) and be
more robust to parameter selection.
Some of the listed "Parameters" and "Attributes" are ussed
in the same manner as HDBSCAN. Differences are stated explicitly.
Parameters
----------
copy_all_inputs: bool, optional (default=True)
Params for MLpack's Euclidian minimum spanning tree (emst) and
Fast Max-Kernel Search (FastMKS).
If specified, all input parameters will be deep copied before the method
is run. This is useful for debugging problems where the input parameters
are being modified by the algorithm, but can slow down the code.
verbose: bool, optional (default=False)
Params for MLpack's Euclidian minimum spanning tree (emst) and
Fast Max-Kernel Search (FastMKS).
Display informational messages and the full list of parameters and
timers at the end of execution.
leaf_size: int, optional (default=20)
Leaf size in the kd-tree built by MLpack's Euclidian minimum spanning
tree (emst) routine. One-element leaves give the empirically best
performance, but at the cost of greater memory requirements.
kernel: str, optional (default=‘gaussian’)
Kernel type to use in FastMKS: ‘gaussian’, ‘epanechnikov’, ‘triangular’ or 'cosine'.
base: float, optional (default=2.0)
Base to use during cover tree construction for MLpack's FastMKS.
min_mode_size : int, optional (default=12)
The minimum size of modal-set; single linkage splits that contain
fewer points than this will be considered points "falling out" of a
modal-set rather than a modal-set splitting into two new modal-sets.
min_cluster_size : int, optional (default=60)
The minimum size of clusters; single linkage splits that contain
fewer points than this will be considered points "falling out" of a
cluster rather than a cluster splitting into two new clusters.
min_samples : int, optional (default=5)
The number of samples in a neighborhood for a point (k-nn)
to be considered as a core point. This includes the point itself.
cluster_selection_method : string, optional (default='eom')
The method used to select clusters from the condensed tree. Similar to
the HDBSCAN*, HQSHIFT uses an "Excess of Mass" algorithm to find the most
persistent clusters. Alternatively you can instead select the clusters
at the leaves of the tree -- this provides the most fine grained and
homogeneous clusters. Routines imported directly from HDBSCAN.
Options are:
* ``eom``
* ``leaf``
allow_single_cluster : bool, optional (default=False)
Similar to HDBSCAN*, HQSHIFT by default will not produce a single cluster,
setting this to True will override this and allow single cluster results in
the case that you feel this is a valid result for your dataset.
Attributes
----------
labels_ : ndarray, shape (n_samples, )
Cluster labels for each point in the dataset given to fit().
Noisy samples are given the label -1.
probabilities_ : ndarray, shape (n_samples, )
Differing from HDBSCAN*, HQSHIFT estimates the probability density
of each sample with FastMKS using different kernels.
cluster_persistence_ : ndarray, shape (n_clusters, )
A score of how persistent each cluster is. A score of 1.0 represents
a perfectly stable cluster that persists over all distance scales,
while a score of 0.0 represents a perfectly ephemeral cluster. These
scores can be guage the relative coherence of the clusters output
by the algorithm.
_condensed_tree : CondensedTree object
The condensed tree produced by HDBSCAN. The object has methods
for converting to pandas, networkx, and plotting.
_single_linkage_tree : SingleLinkageTree object
The single linkage tree produced by HDBSCAN. The object has methods
for converting to pandas, networkx, and plotting.
_emst : Euclidian MinimumSpanningTree object
The euclidian minimum spanning tree generated by HQSHIFT to estimate
optimal kernel bandwidth.
References
----------
.. [1] L. McInnes, J. Healy, S. Astels, hdbscan: Hierarchical density
based clustering In: Journal of Open Source Software, The Open Journal,
volume 2, number 11. 2017
.. [2] Campello, R. J., Moulavi, D., & Sander, J. (2013, April).
Density-based clustering based on hierarchical density estimates.
In Pacific-Asia Conference on Knowledge Discovery and Data Mining
(pp. 160-172). Springer Berlin Heidelberg.
.. [3] Vedaldi A., Soatto S. (2008) Quick Shift and Kernel Methods for
Mode Seeking. In: Forsyth D., Torr P., Zisserman A. (eds) Computer Vision – ECCV 2008.
Lecture Notes in Computer Science, vol 5305. Springer, Berlin, Heidelberg.
.. [4] March, William B. and Ram, Parikshit and Gray, Alexander G.
Fast Euclidean minimum spanning tree: algorithm, analysis, and applications.
Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery
and Data Mining (KDD '10), pp. 603-612, (2010), ACM
.. [5] Curtin, Ryan R. and Ram, Parikshit and Gray, Alexander G.
Fast Exact Max-Kernel Search, Proceedings of the 2013 SIAM International Conference
on Data Mining (SDM '13), 2013, pp.1-9
.. [6] Curtin, Ryan R. and Ram, Parikshit.
Dual-tree fast exact max-kernel search, Statistical Analysis and Data Mining, vol 7,
Wiley Subscription Services, Inc., A Wiley Company, pp. 229-253, 2014
.. [7] Sreevani, C.A. Murthy.
On bandwidth selection using minimal spanning tree for kernel density estimation,
Computational Statistics and Data Analysis 102 (2016), pp.67–84
"""
def __init__(self, copy_all_inputs=True, verbose=False,
leaf_size=20, kernel='gaussian', base=2.0,
min_cluster_size=60, min_mode_size=12, min_samples=5,
cluster_selection_method='eom', allow_single_cluster=False):
self.copy_all_inputs = copy_all_inputs
self.verbose = verbose
self.leaf_size = leaf_size
self.base = base
self.kernel = kernel
self.min_mode_size = min_mode_size
self.min_cluster_size = min_cluster_size
self.min_samples = min_samples
self.cluster_selection_method = cluster_selection_method
self.allow_single_cluster = allow_single_cluster
self._emst = None
self._mrmst = None
self._condensed_tree = None
self._single_linkage_tree = None
def _compute_medoids(self, dist_matrix, weight_matrix=None):
if weight_matrix is None:
weight_matrix = rbf_kernel(d=dist_matrix, bw=self._emst._bandwidth)
P = np.sum(weight_matrix, axis=0)
prob = P
P = P[:, None] - P
dist_matrix[dist_matrix==0] = np.max(dist_matrix)/2
S = np.sign(P) * (1/dist_matrix)
medoids = np.argmax(S, axis=0)
return medoids, prob
def _quick_shift(self, data, dist_matrix, weight_matrix=None):
medoids,prob= self._compute_medoids(dist_matrix, weight_matrix)
stationary_idx = []
for i in range(len(medoids)):
if medoids[i] == i:
stationary_idx.append(i)
cls_ctrs = data[stationary_idx]
labels, traj_lst = [],[]
labels_val = {}
lab = 0
for i in stationary_idx:
labels_val[i] = lab
lab += 1
for i in range(len(data)):
next_med = medoids[i]
traj = []
while next_med not in stationary_idx:
traj.append(next_med.item())
next_med = medoids[next_med]
traj.append(next_med.item())
traj_lst.append(np.asarray(traj))
labels.append(labels_val[next_med.item()])
return cls_ctrs, np.asarray(labels), np.asarray(stationary_idx),\
np.asarray(traj_lst), np.asarray(prob)
def _quick_shift_fastmks(self, data):
# for each point in the query set, the k points in the reference set
# with maximum kernel evaluations are found
output = fastmks(k=data.shape[0], reference=data, bandwidth=self._emst.bandwidth,
base=self.base, kernel=self.kernel,
copy_all_inputs=self.copy_all_inputs)
indices = output['indices']
kernels = output['kernels']
indices[:,-1]=np.arange(indices.shape[0])
sorted_indices = np.argsort(indices)
kernel_srt = np.array([[kernels[ri, col]] for ri,row in enumerate(sorted_indices)
for col in row]).reshape(indices.shape)
#small_number = _find_nth_smallest(np.ravel(kernel_srt),indices.shape[0]+1)
small_number = np.median(kernel_srt)
np.fill_diagonal(kernel_srt, small_number)
# return a pseudo distance as the inverse of evaluations (val) of kernels
#dist_matrix = np.sqrt(-log(kernel_srt+np.finfo(float).eps)*self._emst.bandwidth)*self._emst.bandwidth
dist_matrix = -log(kernel_srt+np.finfo(float).eps)*self._emst.bandwidth*self._emst.bandwidth
return dist_matrix, kernel_srt
def _construct_graph(self, X):
dist_matrix, weight_matrix = self._quick_shift_fastmks(data=X)
cls_ctrs, cls_labels, stat_idx, traj, prob =\
self._quick_shift(data=X, dist_matrix=dist_matrix, weight_matrix=weight_matrix)
self.paths = [path.tolist() for path in traj]
# Hierarchical Quick Shift Tree
visited = np.zeros(len(self.paths))
self._hqst = []
for i, pth in enumerate(self.paths):
# no edges inserted between stationary node and itsef!
if i != stat_idx[0]:
# append the current node to the beginning of the path
cpth = [i] + pth
for j in np.arange(len(cpth)-1):
self._hqst.append([cpth[j], cpth[j+1], dist_matrix[cpth[j], cpth[j+1]]])
self._hqst = np.array(self._hqst)
_hqst_sparse = csr_matrix((self._hqst.T[2], (self._hqst.T[0], self._hqst.T[1])),
shape=dist_matrix.shape)
# Compute the minimum spanning tree for the sparse graph
self._hqst_mst = csgraph.minimum_spanning_tree(_hqst_sparse)
# Convert the graph to scipy cluster array format
nonzeros = self._hqst_mst.nonzero()
nonzero_vals = self._hqst_mst[nonzeros]
self._hqst_mst = np.vstack(nonzeros + (nonzero_vals,)).T
# Sort edges of the min_spanning_tree by weight
self._hqst_mst = np.asarray(self._hqst_mst[np.argsort(self._hqst_mst.T[2]),:][0])
# Perform single linkage Hierarchical Quick Shift Minimum Spanning Tree
sltree = label(self._hqst_mst)
(self.quick_shift_labels_,
self.quick_shift_probabilities_,
self.quick_shift_cluster_persistence,
self._quick_shift_condensed_tree,
self._quick_shift_single_linkage_tree) =\
(_tree_to_labels(X=X, single_linkage_tree=sltree,
cluster_selection_method=self.cluster_selection_method,
allow_single_cluster=self.allow_single_cluster,
min_cluster_size=self.min_mode_size))
self.find_idx_roots(X)
# Perform single linkage Mutual Reachability Minimum Spanning Tree
sltree = label(self._mrmst.mrmst_graph)
(self.labels_,
self.probabilities_,
self.cluster_persistence,
self._condensed_tree,
self._single_linkage_tree) =\
(_tree_to_labels(X=X, single_linkage_tree=sltree,
cluster_selection_method=self.cluster_selection_method,
allow_single_cluster=self.allow_single_cluster,
min_cluster_size=self.min_cluster_size))
def hqshift(self, X):
# sanity checks
if self.leaf_size < 1:
raise ValueError('leaf size shall be greater than 0')
if self.kernel not in ['gaussian', 'epanechnikov', 'triangular', 'cosine']:
raise ValueError('Invalid kernel: %s\n'
'Shall be one of: "gaussian", "epanechnikov", "triangular", "cosine"\n' )
if type(self.min_samples) is not int or type(self.min_cluster_size) is not int:
raise ValueError('Min samples and min cluster size must be integers!')
if self.min_samples <= 0 or self.min_cluster_size <= 0:
raise ValueError('min cluster size shall be positive integer')
if self.cluster_selection_method not in ('eom', 'leaf'):
raise ValueError('Invalid cluster selection method: %s\n'
'Shall be one of: "eom", "leaf"\n')
# construct Euclidian Minimum Spanning Tree for bandwidth estimation
# bandwidth to use in FastMKS for Gaussian, Epanechnikov, and triangular kernels.
# this is the "spread" of the kernel.
self._emst = EuclideanMinimumSpanningTree(data=X, copy_all_inputs=self.copy_all_inputs,
verbose=self.verbose, leaf_size=self.leaf_size)
self._mrmst = MutualReachabilityMinimumSpanningTree(data=X, min_samples=self.min_samples,
leaf_size=self.leaf_size)
self._construct_graph(X=X)
def plot_mrmst(self, X, axis=None, with_labels=False, node_size=40, alpha=0.5):
"""Plot the mutual reachability minimum spanning tree with labels.
Parameters
----------
axis: matplotlib axis, optional (default=None)
The axis to render the plot to
with_labels: bool, optional (default=False)
Set to True to draw labels on the nodes.
node_size: int, optional (default 40)
The size of nodes in the plot.
alpha: float, the edge transparency (default 0.5)
Returns
-------
axis : matplotlib axis
The axis used the render the plot.
"""
self._G = nx.Graph()
nodes = np.arange(X.shape[0])
self._G.add_nodes_from(nodes)
for row in self._mrmst.mrmst_graph:
self._G.add_edge(row[0], row[1], weight=row[2])
data_dict = {index: tuple(row) for index, row in enumerate(X)}
nx.set_node_attributes(self._G, data_dict, 'node_pos')
if axis is None:
axis = plt.gca()
node_pos = nx.get_node_attributes(self._G, 'node_pos')
max_n_labels = np.maximum(8, np.unique(self.labels_).max() + 1)
palette = sns.hls_palette(max_n_labels, l=.3, s=.8)
node_color = [palette[x] if x >= 0 else (0.0, 0.0, 0.0) for x in self.labels_]
nx.draw_networkx(self._G, ax=axis, pos=node_pos, with_labels=False, alpha=alpha,
node_color=node_color, node_size=node_size)
# plt.title('HQuickSHIFT Mutual Reachability Graph, number of Clusters: {:d}'.format(\
# np.unique(self.labels_).max() + 1), fontsize=28)
plt.setp(plt.gca().get_xticklabels(), visible=False)
plt.setp(plt.gca().get_yticklabels(), visible=False)
return axis
def find_idx_roots(self, X):
self._Di_G = nx.DiGraph()
nodes = np.arange(X.shape[0])
self._Di_G.add_nodes_from(nodes)
for row in self._hqst_mst:
self._Di_G.add_edge(row[0], row[1], weight=row[2])
data_dict = {index: tuple(row) for index, row in enumerate(X)}
total_n_clusters = np.max(self.quick_shift_labels_) + 1
cluster_range = np.arange(-1, total_n_clusters)\
if -1 in self.quick_shift_labels_ else np.arange(total_n_clusters)
# Find root node of each subcluster including noise with a node (out_degree==0)
self._idx_root = []
for idx_clst in cluster_range:
sub_nodes = np.where(self.quick_shift_labels_==idx_clst)[0]
sub_G = nx.DiGraph(self._Di_G.subgraph(sub_nodes))
idx_subg_root = list(node for node, out_degree in sub_G.out_degree()
if out_degree == 0)[0]
self._idx_root.append(idx_subg_root)
def plot(self, X, axis=None, with_labels=False, node_size=40, alpha=0.5,
plot_edge=True, plot_hull=True, file_name=None):
"""Plot the hierarchical quick shift tree with labels.
Parameters
----------
axis: matplotlib axis, optional (default None)
The axis to render the plot to
with_labels: bool, optional (default False)
Set to True to draw labels on the nodes.
node_size: int, optional (default 40)
The size of nodes in the plot.
alpha: float, the edge transparency (default 0.5)
plot_edge: bool, optional (default True)
plot_hull: bool, optional (default True)
Returns
-------
axis : matplotlib axis
The axis used the render the plot.
"""
self._Di_G = nx.DiGraph()
nodes = np.arange(X.shape[0])
self._Di_G.add_nodes_from(nodes)
for row in self._hqst_mst:
self._Di_G.add_edge(row[0], row[1], weight=row[2])
data_dict = {index: tuple(row) for index, row in enumerate(X)}
nx.set_node_attributes(self._Di_G, data_dict, 'node_pos')
if axis is None:
axis = plt.gca()
node_pos = nx.get_node_attributes(self._Di_G, 'node_pos')
#palette = plt.cm.tab10(np.linspace(0, 1, np.unique(self.labels_).max() + 1))
#palette = sns.color_palette('deep', np.unique(self.labels_).max() + 1)
max_n_labels = np.maximum(8, np.unique(self.labels_).max() + 1)
palette = sns.hls_palette(max_n_labels, l=.3, s=.8)
node_color = [palette[x] if x >= 0 else (0.0, 0.0, 0.0) for x in self.labels_]
total_n_clusters = np.max(self.quick_shift_labels_) + 1
cluster_range = np.arange(-1, total_n_clusters)\
if -1 in self.quick_shift_labels_ else np.arange(total_n_clusters)
# Find root node of each subcluster including noise with a node (out_degree==0)
self._idx_root = []
for idx_clst in cluster_range:
sub_nodes = np.where(self.quick_shift_labels_==idx_clst)[0]
sub_G = nx.DiGraph(self._Di_G.subgraph(sub_nodes))
idx_subg_root = list(node for node, out_degree in sub_G.out_degree()
if out_degree == 0)[0]
axis.plot((X[idx_subg_root][0]), (X[idx_subg_root][1]), 'X', color='k', markersize=12)
self._idx_root.append(idx_subg_root)
if plot_hull:
node_labels = self.labels_[sub_nodes]
points=X[sub_nodes][node_labels!=-1]
hull = ConvexHull(points)
for simplex in hull.simplices:
axis.plot(points[simplex, 0], points[simplex, 1], 'k--')
#self._Di_G.remove_edges_from(list(self._Di_G.edges(_idx_root)))
#edge_weights = list(nx.get_edge_attributes(self._Di_G,'weight').values())
edge_weights = 2
if not plot_edge:
self._Di_G.remove_edges_from(list(self._Di_G.edges(nodes)))
nx.draw_networkx(self._Di_G, ax=axis, pos=node_pos, with_labels=False, alpha=alpha,
node_color=node_color, node_size=node_size, width=edge_weights,
arrowsize=14, arrowstyle='->')
# plt.title('HQSHIFT Mode Attraction Graph, # of Modes: {:d}, # of Clusters: {:d}'.format(\
# np.unique(self.quick_shift_labels_).max() + 1, np.unique(self.labels_).max() + 1),
# fontsize=28)
# if X.shape[1] == 2:
# plt.xlabel("$x_0$", fontsize=28)
# plt.ylabel("$x_1$", fontsize=28, rotation=90)
plt.setp(axis.get_xticklabels(), visible=False)
plt.setp(axis.get_yticklabels(), visible=False)
plt.tight_layout()
if file_name is not None: plt.savefig(file_name, dpi=200, bbox_inches='tight')
return axis
def fit(self, X, y=None):
"""Perform HQSHIFT clustering from features.
Parameters
----------
X : array of shape (n_samples, n_features)
Returns
-------
self : object
Returns self
"""
self.hqshift(X)
return self
def fit_predict(self, X, y=None):
"""Performs clustering on X and returns cluster labels.
Parameters
----------
X : array of shape (n_samples, n_features)
Returns
-------
y : ndarray, shape (n_samples, )
cluster labels
"""
self.fit(X)
return self.labels_
@property
def single_linkage_tree(self):
if self._single_linkage_tree is not None:
return SingleLinkageTree(self._single_linkage_tree)
else:
raise AttributeError('No single linkage tree was generated; try running fit'
' first.')
@property
def condensed_tree(self):
if self._condensed_tree is not None:
return CondensedTree(self._condensed_tree,
self.cluster_selection_method,
self.allow_single_cluster)
else:
raise AttributeError('No condensed tree was generated; try running fit first.')
@property
def quick_shift_condensed_tree(self):
if self._quick_shift_condensed_tree is not None:
return CondensedTree(self._quick_shift_condensed_tree,
self.cluster_selection_method,
self.allow_single_cluster)
else:
raise AttributeError('No quickshift condensed tree was generated; try running fit first.')
With a working example, we can demonstrate the features of Hierarchical Quick Shift algorithm and its advantages over QuickshiftPP. First, we create a toy dataset:
Prepare Dataset for RNN¶
In [18]:
n_samples=350
n_noise_pts = 600
add_uniform_noise = True
def generate_data(n_samples, add_uniform_noise=add_uniform_noise,
plot_data=True, plt_title=None, file_name=None):
random_seed(seed_value=seed, use_cuda=use_cuda)
moons, lbls_moons = data.make_moons(n_samples=n_samples, noise=0.05, random_state=seed)
blobs, lbls_blobs = data.make_blobs(n_samples=n_samples, centers=[(-1.5, 3.0), (1.5, 3.0)],
cluster_std=[0.15, 0.25], random_state=seed)
# shear angles
theta = np.radians([30, -30, -30, 30])
t = np.tan(theta)
blob3, lbls_blob3 = data.make_blobs(n_samples=n_samples//3,
centers=[(-0.95, 0.75)],
cluster_std=[0.10], random_state=seed)
shear_x = np.array(((1, 0), (t[0], 1))).T
blob3 = blob3.dot(shear_x)
blob4, lbls_blob4 = data.make_blobs(n_samples=n_samples//4,
centers=[(0.95, 0.75)],
cluster_std=[0.10], random_state=seed)
shear_x = np.array(((1, 0), (t[1], 1))).T
blob4 = blob4.dot(shear_x)
blob5, lbls_blob5 = data.make_blobs(n_samples=n_samples//4,
centers=[(0.15, 0.0)],
cluster_std=[0.10], random_state=seed)
shear_x = np.array(((1, t[2]), (0, 1))).T
blob5 = blob5.dot(shear_x)
blob6, lbls_blob6 = data.make_blobs(n_samples=n_samples//3,
centers=[(1.85, 0.0)],
cluster_std=[0.10], random_state=seed)
shear_x = np.array(((1, t[3]), (0, 1))).T
blob6 = blob6.dot(shear_x)
#import pdb;pdb.set_trace()
test_data = np.vstack([moons, blobs, blob3, blob4, blob5, blob6])
test_data_lbls = np.hstack([lbls_moons, lbls_blobs+2,
lbls_blob3+4, lbls_blob4+5, lbls_blob5+6, lbls_blob6+7])
if add_uniform_noise:
assert(test_data.shape[1]==2)
(min0, min1), (max_0, max_1) = np.min(test_data, axis=0), np.max(test_data, axis=0)
noise_data = np.random.RandomState(seed).uniform([min0, min1],[max_0, max_1], size=(n_noise_pts, 2))
noise_lbls = np.array([-1]*n_noise_pts)
test_data = np.vstack([test_data, noise_data])
test_data_lbls = np.hstack([test_data_lbls, noise_lbls])
test_data = shuffle(test_data, random_state=seed)
test_data_lbls = shuffle(test_data_lbls, random_state=seed)
# normalize dataset for easier parameter selection
test_data_std = StandardScaler().fit_transform(test_data)
#test_data_std = test_data
#import pdb;pdb.set_trace()
if plot_data:
plot_clusters(data=test_data_std, labels=test_data_lbls,
file_name=file_name, plt_title=plt_title)
return test_data_std
In [19]:
test_data = generate_data(n_samples=n_samples, add_uniform_noise=True,
file_name='blob_moon_dataset.png')
curaxes = plt.gca()
ax_xlim = curaxes.get_xlim()
ax_ylim = curaxes.get_ylim()
In [20]:
from QuickshiftPP import *
plot_clusters(data=test_data, algorithm=QuickshiftPP,
args=(), kwds={'k':20, 'beta':.7}, file_name='blob_moon_qspp.png')
curaxes = plt.gca()
curaxes.set_xlim(ax_xlim)
curaxes.set_ylim(ax_ylim)
plt.show()
In [21]:
hqshift = HQSHIFT(min_cluster_size=125,
min_mode_size=150,
min_samples=15,
allow_single_cluster=False).fit(test_data)
plt.figure(figsize=fig_size)
curaxes = plt.gca()
curaxes.set_xlim(ax_xlim)
curaxes.set_ylim(ax_ylim)
hqshift.plot(test_data, plot_edge=True, plot_hull=True, node_size=30, alpha=0.4,
file_name='blob_moon_hqshift.png')
plt.show()
In [22]:
plt.figure(figsize=fig_size)
hqshift._emst.plot()
print(hqshift._emst.bandwidth)
plt.savefig('emst.png', bbox_inches='tight')
plt.show()
0.20421907202880743
In [23]:
plt.figure(figsize=fig_size)
hqshift._mrmst.plot()
plt.savefig('mrmst.png', bbox_inches='tight')
plt.show()
In [24]:
plt.figure(figsize=(16,12))
max_n_labels = np.maximum(8, np.unique(hqshift.labels_).max() + 1)
palette = sns.hls_palette(max_n_labels, l=.3, s=.8)
hqshift.condensed_tree.plot(select_clusters=True,
selection_palette=palette,
log_size=False)
plt.setp(plt.gca().get_xticklabels(), visible=False)
plt.setp(plt.gca().get_yticklabels(), visible=False)
plt.tight_layout()
plt.show()
In [25]:
plt.figure(figsize=(16,12))
max_n_labels = np.maximum(8, np.unique(hqshift.labels_).max() + 1)
palette = sns.hls_palette(max_n_labels, l=.3, s=.8)
hqshift.quick_shift_condensed_tree.plot(select_clusters=False,
selection_palette=palette,
log_size=False)
plt.setp(plt.gca().get_xticklabels(), visible=False)
plt.setp(plt.gca().get_yticklabels(), visible=False)
plt.ylim(0.025)
plt.tight_layout()
plt.show()
Recurrent Neural Network (RNN)¶
- HQuickShift restricts the steps to sample points (and thus is a sample-based version of Mean Shift).
- What happens if we have a new single point to cluster? How to assign it to a mode? Re-run the HQuickShift?
- Or find the nn in sample set and go forward? Then we need to store all data samples!
- Samples follow common paths towards to the local mode.
In [14]:
len(hqshift.paths), hqshift._idx_root
Out[14]:
(1706, [1178, 1479, 785, 968, 493, 1615])
Train a RNN from Scratch¶
In [15]:
clust_paths = [[j]+pth[:pth.index(idx)+1] for idx in hqshift._idx_root
for j, pth in enumerate(hqshift.paths) if idx in pth]
clust_data = [test_data[pth] for pth in clust_paths]
clust_paths
Out[15]:
[[13, 627, 1309, 755, 1293, 628, 1690, 1281, 477, 765, 1459, 1178], [23, 142, 1471, 13, 627, 1309, 755, 1293, 628, 1690, 1281, 477, 765, 1459, 1178], [24, 633, 946, 1703, 1015, 453, 568, 858, 340, 649, 945, 1087, 634, 822, 1319, 884, 730, 259, 1497, 1700, 134, 765, 1459, 1178], [25, 323, 434, 691, 1329, 827, 1107, 147, 1163, 1178], [28, 78, 510, 544, 1253, 1257, 1415, 1266, 125, 427, 1381, 509, 1642, 932, 623, 29, 1036, 551, 970, 198, 1653, 1131, 306, 1039, 1325, 1416, 1459, 1178], [29, 1036, 551, 970, 198, 1653, 1131, 306, 1039, 1325, 1416, 1459, 1178], [33, 379, 1447, 1009, 1699, 264, 1653, 1131, 306, 1039, 1325, 1416, 1459, 1178], [38, 395, 267, 174, 1449, 453, 568, 858, 340, 649, 945, 1087, 634, 822, 1319, 884, 730, 259, 1497, 1700, 134, 765, 1459, 1178], [51, 1344, 840, 838, 165, 670, 288, 25, 323, 434, 691, 1329, 827, 1107, 147, 1163, 1178], [71, 705, 1085, 1437, 1175, 74, 477, 765, 1459, 1178], [73, 1163, 1178], [74, 477, 765, 1459, 1178], [77, 470, 1537, 907, 73, 1163, 1178], [78, 510, 544, 1253, 1257, 1415, 1266, 125, 427, 1381, 509, 1642, 932, 623, 29, 1036, 551, 970, 198, 1653, 1131, 306, 1039, 1325, 1416, 1459, 1178], [95, 205, 38, 395, 267, 174, 1449, 453, 568, 858, 340, 649, 945, 1087, 634, 822, 1319, 884, 730, 259, 1497, 1700, 134, 765, 1459, 1178], [97, 1377, 95, 205, 38, 395, 267, 174, 1449, 453, 568, 858, 340, 649, 945, 1087, 634, 822, 1319, 884, 730, 259, 1497, 1700, 134, 765, 1459, 1178], [114, 432, 314, 221, 594, 1309, 755, 1293, 628, 1690, 1281, 477, 765, 1459, 1178], [125, 427, 1381, 509, 1642, 932, 623, 29, 1036, 551, 970, 198, 1653, 1131, 306, 1039, 1325, 1416, 1459, 1178], [131, 1172, 898, 1417, 1398, 921, 71, 705, 1085, 1437, 1175, 74, 477, 765, 1459, 1178], [134, 765, 1459, 1178], [142, 1471, 13, 627, 1309, 755, 1293, 628, 1690, 1281, 477, 765, 1459, 1178], [147, 1163, 1178], [165, 670, 288, 25, 323, 434, 691, 1329, 827, 1107, 147, 1163, 1178], [167, 23, 142, 1471, 13, 627, 1309, 755, 1293, 628, 1690, 1281, 477, 765, 1459, 1178], [174, 1449, 453, 568, 858, 340, 649, 945, 1087, 634, 822, 1319, 884, 730, 259, 1497, 1700, 134, 765, 1459, 1178], [185, 1015, 453, 568, 858, 340, 649, 945, 1087, 634, 822, 1319, 884, 730, 259, 1497, 1700, 134, 765, 1459, 1178], [198, 1653, 1131, 306, 1039, 1325, 1416, 1459, 1178], [200, 1140, 1673, 775, 293, 1688, 659, 1049, 471, 956, 1483, 1178], [201, 822, 1319, 884, 730, 259, 1497, 1700, 134, 765, 1459, 1178], [205, 38, 395, 267, 174, 1449, 453, 568, 858, 340, 649, 945, 1087, 634, 822, 1319, 884, 730, 259, 1497, 1700, 134, 765, 1459, 1178], [220, 524, 830, 221, 594, 1309, 755, 1293, 628, 1690, 1281, 477, 765, 1459, 1178], [221, 594, 1309, 755, 1293, 628, 1690, 1281, 477, 765, 1459, 1178], [223, 1398, 921, 71, 705, 1085, 1437, 1175, 74, 477, 765, 1459, 1178], [240, 1344, 840, 838, 165, 670, 288, 25, 323, 434, 691, 1329, 827, 1107, 147, 1163, 1178], [249, 505, 1347, 589, 1080, 288, 25, 323, 434, 691, 1329, 827, 1107, 147, 1163, 1178], [259, 1497, 1700, 134, 765, 1459, 1178], [261, 505, 1347, 589, 1080, 288, 25, 323, 434, 691, 1329, 827, 1107, 147, 1163, 1178], [264, 1653, 1131, 306, 1039, 1325, 1416, 1459, 1178], [267, 174, 1449, 453, 568, 858, 340, 649, 945, 1087, 634, 822, 1319, 884, 730, 259, 1497, 1700, 134, 765, 1459, 1178], [283, 972, 1355, 1062, 131, 1172, 898, 1417, 1398, 921, 71, 705, 1085, 1437, 1175, 74, 477, 765, 1459, 1178], [285, 1591, 371, 142, 1471, 13, 627, 1309, 755, 1293, 628, 1690, 1281, 477, 765, 1459, 1178], [288, 25, 323, 434, 691, 1329, 827, 1107, 147, 1163, 1178], [291, 1565, 379, 1447, 1009, 1699, 264, 1653, 1131, 306, 1039, 1325, 1416, 1459, 1178], [293, 1688, 659, 1049, 471, 956, 1483, 1178], [298, 1272, 1302, 1252, 1148, 1390, 1588, 659, 1049, 471, 956, 1483, 1178], [306, 1039, 1325, 1416, 1459, 1178], [307, 201, 822, 1319, 884, 730, 259, 1497, 1700, 134, 765, 1459, 1178], [314, 221, 594, 1309, 755, 1293, 628, 1690, 1281, 477, 765, 1459, 1178], [323, 434, 691, 1329, 827, 1107, 147, 1163, 1178], [329, 589, 1080, 288, 25, 323, 434, 691, 1329, 827, 1107, 147, 1163, 1178], [330, 504, 223, 1398, 921, 71, 705, 1085, 1437, 1175, 74, 477, 765, 1459, 1178], [340, 649, 945, 1087, 634, 822, 1319, 884, 730, 259, 1497, 1700, 134, 765, 1459, 1178], [345, 1252, 1148, 1390, 1588, 659, 1049, 471, 956, 1483, 1178], [348, 1560, 925, 1009, 1699, 264, 1653, 1131, 306, 1039, 1325, 1416, 1459, 1178], [364, 831, 1329, 827, 1107, 147, 1163, 1178], [368, 605, 767, 1151, 903, 97, 1377, 95, 205, 38, 395, 267, 174, 1449, 453, 568, 858, 340, 649, 945, 1087, 634, 822, 1319, 884, 730, 259, 1497, 1700, 134, 765, 1459, 1178], [371, 142, 1471, 13, 627, 1309, 755, 1293, 628, 1690, 1281, 477, 765, 1459, 1178], [379, 1447, 1009, 1699, 264, 1653, 1131, 306, 1039, 1325, 1416, 1459, 1178], [381, 910, 205, 38, 395, 267, 174, 1449, 453, 568, 858, 340, 649, 945, 1087, 634, 822, 1319, 884, 730, 259, 1497, 1700, 134, 765, 1459, 1178], [395, 267, 174, 1449, 453, 568, 858, 340, 649, 945, 1087, 634, 822, 1319, 884, 730, 259, 1497, 1700, 134, 765, 1459, 1178], 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649, 945, 1087, 634, 822, 1319, 884, 730, 259, 1497, 1700, 134, 765, 1459, 1178, 774, 1068, 760, 1229, 874, 324, 937, 483, 555, 1400, 1446, 493, 1343, 856, 1491, 98, 268, 59, 637, 352, 1498, 597, 1615, 1340, 244, 1128, 49, 1146, 1179, 1372, 906, 908, 968, 892, 880, 354, 997, 1590, 136, 963, 785], [454, 790, 1251, 313, 122, 507, 48, 824, 996, 1089, 607, 116, 1595, 857, 620, 1212, 128, 159, 760, 1229, 874, 324, 937, 483, 555, 1400, 1446, 493, 1343, 856, 1491, 98, 268, 59, 637, 352, 1498, 597, 1615, 1340, 244, 1128, 49, 1146, 1179, 1372, 906, 908, 968, 892, 880, 354, 997, 1590, 136, 963, 785], [455, 96, 507, 48, 824, 996, 1089, 607, 116, 1595, 857, 620, 1212, 128, 159, 760, 1229, 874, 324, 937, 483, 555, 1400, 1446, 493, 1343, 856, 1491, 98, 268, 59, 637, 352, 1498, 597, 1615, 1340, 244, 1128, 49, 1146, 1179, 1372, 906, 908, 968, 892, 880, 354, 997, 1590, 136, 963, 785], [456, 677, 163, 1592, 1365, 1603, 372, 452, 332, 892, 880, 354, 997, 1590, 136, 963, 785], [457, 163, 1592, 1365, 1603, 372, 452, 332, 892, 880, 354, 997, 1590, 136, 963, 785], [458, 435, 109, 539, 1589, 328, 309, 493, 1343, 856, 1491, 98, 268, 59, 637, 352, 1498, 597, 1615, 1340, 244, 1128, 49, 1146, 1179, 1372, 906, 908, 968, 892, 880, 354, 997, 1590, 136, 963, 785], [459, 315, 1478, 1594, 876, 962, 1190, 1341, 273, 581, 1606, 132, 1170, 104, 1555, 1536, 747, 1290, 1411, 1042, 444, 916, 392, 98, 268, 59, 637, 352, 1498, 597, 1615, 1340, 244, 1128, 49, 1146, 1179, 1372, 906, 908, 968, 892, 880, 354, 997, 1590, 136, 963, 785], [460, 276, 645, 99, 560, 1461, 464, 152, 1516, 963, 785], ...]
In [16]:
# Construct Vocabulary, make sure BOS (<xxbos>) idx is 0
vocab_tok = [BOS] + [str(idx) for idx in np.arange(test_data.shape[0])]
# => ['<pad>', '0', '1', '2', '3',... 'seq_len-1']
vocab_ = {idx:tok for idx,tok in enumerate(vocab_tok)}
ids = [[0]+list(np.array(pth)+1) for pth in clust_paths]
ids[150], ids[-1], vocab_[ids[-1][-1]]
Out[16]:
([0, 869, 1592, 372, 143, 1472, 14, 628, 1310, 756, 1294, 629, 1691, 1282, 478, 766, 1460, 1179], [0, 1704, 1016, 454, 569, 859, 341, 650, 946, 1088, 635, 823, 1320, 885, 731, 260, 1498, 1701, 135, 766, 1460, 1179, 775, 1069, 761, 1230, 875, 325, 938, 484, 556, 1401, 1447, 494, 1344, 857, 1492, 99, 269, 60, 638, 353, 1499, 598, 1616], '1615')
In [17]:
vocab_tok[0], vocab_tok[-1], list(vocab_.items())[0], list(vocab_.items())[-1]
Out[17]:
('xxbos', '1705', (0, 'xxbos'), (1706, '1705'))
In [18]:
nh=2
nt=128
bs=128
bptt=40
vocab=Vocab(vocab_)
pretrained_weights = np.vstack(([10.0, 10.0], test_data))
nv = len(vocab.itos); nv
Out[18]:
1707
In [20]:
class Model_Density(nn.Module):
def __init__(self, nv, nh, bs):
super().__init__()
self.nv = nv
self.nh = nh
self.nt = nt
self.bs = bs
self.i_h = nn.Embedding(self.nv,self.nh)
# intialize the word vectors, pretrained_weights is a
# numpy array of size (vocab_size, hidden_size) and
# pretrained_weights[i] retrieves the word vector of
# i-th word in the vocabulary
self.i_h.weight.data.copy_(torch.from_numpy(pretrained_weights))
self.i_h.weight.requires_grad = False
self.h_t = nn.Linear(self.nh,self.nt)
self.rnn = nn.GRU(self.nt, self.nt, 2, batch_first=True)
self.h_o = nn.Linear(self.nt,self.nv)
self.bn = BatchNorm1dFlat(self.nt)
self.reset()
def reset(self):
self.h = torch.zeros(2, self.bs, self.nt).cuda()
def forward(self, x):
#import pdb;pdb.set_trace()
res,h = self.rnn(self.h_t(self.i_h(x)), self.h)
self.h = h.detach()
return self.h_o(self.bn(res))
def forward_with_embedded(self, x):
res,h = self.rnn(self.h_t(x), self.h)
self.h = h.detach()
return self.h_o(self.bn(res))
In [21]:
ids = np.array(ids)
data = TextLMDataBunch.from_ids('/tmp', vocab=vocab,
train_ids=ids, valid_ids=ids,
bs=bs, bptt=bptt, device=device)
data
Out[21]:
TextLMDataBunch; Train: LabelList (4931 items) x: LMTextList xxbos 13 627 1309 755 1293 628 1690 1281 477 765 1459 1178,xxbos 23 142 1471 13 627 1309 755 1293 628 1690 1281 477 765 1459 1178,xxbos 24 633 946 1703 1015 453 568 858 340 649 945 1087 634 822 1319 884 730 259 1497 1700 134 765 1459 1178,xxbos 25 323 434 691 1329 827 1107 147 1163 1178,xxbos 28 78 510 544 1253 1257 1415 1266 125 427 1381 509 1642 932 623 29 1036 551 970 198 1653 1131 306 1039 1325 1416 1459 1178 y: LMLabelList ,,,, Path: /tmp; Valid: LabelList (4931 items) x: LMTextList xxbos 13 627 1309 755 1293 628 1690 1281 477 765 1459 1178,xxbos 23 142 1471 13 627 1309 755 1293 628 1690 1281 477 765 1459 1178,xxbos 24 633 946 1703 1015 453 568 858 340 649 945 1087 634 822 1319 884 730 259 1497 1700 134 765 1459 1178,xxbos 25 323 434 691 1329 827 1107 147 1163 1178,xxbos 28 78 510 544 1253 1257 1415 1266 125 427 1381 509 1642 932 623 29 1036 551 970 198 1653 1131 306 1039 1325 1416 1459 1178 y: LMLabelList ,,,, Path: /tmp; Test: None
In [22]:
data.valid_ds[0][0], data.bptt, len(data.valid_dl)
Out[22]:
(<fastai.text.data.Text at 0x7f9331b57860>, 40, 33)
In [23]:
it = iter(data.valid_dl)
x1,y1 = next(it)
it.close()
In [24]:
x1[0]
Out[24]:
tensor([ 0, 14, 628, 1310, 756, 1294, 629, 1691, 1282, 478, 766, 1460,
1179, 0, 24, 143, 1472, 14, 628, 1310, 756, 1294, 629, 1691,
1282, 478, 766, 1460, 1179, 0, 25, 634, 947, 1704, 1016, 454,
569, 859, 341, 650], device='cuda:0')
In [25]:
y1[0]
Out[25]:
tensor([ 14, 628, 1310, 756, 1294, 629, 1691, 1282, 478, 766, 1460, 1179,
0, 24, 143, 1472, 14, 628, 1310, 756, 1294, 629, 1691, 1282,
478, 766, 1460, 1179, 0, 25, 634, 947, 1704, 1016, 454, 569,
859, 341, 650, 946], device='cuda:0')
In [26]:
v = data.valid_ds.vocab
v.textify(data.train_ds[0][0].data)
Out[26]:
'xxbos 13 627 1309 755 1293 628 1690 1281 477 765 1459 1178'
In [27]:
random_seed(seed_value=seed, use_cuda=use_cuda)
In [28]:
learn = Learner(data, Model_Density(nv=nv, nh=nh, bs=bs), metrics=accuracy)
In [28]:
learn.lr_find()
learn.recorder.plot()
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
In [29]:
learn.fit_one_cycle(200, 1e-2);
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 6.963598 | 6.794902 | 0.061997 | 00:01 |
| 2 | 6.464911 | 5.819698 | 0.119366 | 00:00 |
| 3 | 5.873365 | 5.014758 | 0.175243 | 00:00 |
| 4 | 5.203681 | 4.212790 | 0.216862 | 00:01 |
| 5 | 4.534028 | 3.603711 | 0.324402 | 00:01 |
| 6 | 3.919159 | 3.043718 | 0.433694 | 00:01 |
| 7 | 3.376612 | 2.603957 | 0.514631 | 00:01 |
| 8 | 2.908641 | 2.259512 | 0.558771 | 00:01 |
| 9 | 2.510895 | 1.953933 | 0.607931 | 00:01 |
| 10 | 2.177735 | 1.715029 | 0.640229 | 00:01 |
| 11 | 1.902415 | 1.521286 | 0.669354 | 00:01 |
| 12 | 1.671714 | 1.366033 | 0.697195 | 00:01 |
| 13 | 1.498700 | 1.275100 | 0.700142 | 00:01 |
| 14 | 1.348517 | 1.146204 | 0.720307 | 00:01 |
| 15 | 1.220911 | 1.040214 | 0.751497 | 00:01 |
| 16 | 1.111490 | 0.959446 | 0.771893 | 00:01 |
| 17 | 1.021913 | 0.939835 | 0.756729 | 00:01 |
| 18 | 0.951478 | 0.828187 | 0.796117 | 00:01 |
| 19 | 0.881467 | 0.781293 | 0.798183 | 00:01 |
| 20 | 0.821287 | 0.736811 | 0.813494 | 00:01 |
| 21 | 0.776340 | 0.700635 | 0.825302 | 00:01 |
| 22 | 0.727407 | 0.666202 | 0.827752 | 00:01 |
| 23 | 0.689344 | 0.642456 | 0.836015 | 00:01 |
| 24 | 0.657872 | 0.621062 | 0.843572 | 00:01 |
| 25 | 0.626212 | 0.546793 | 0.867519 | 00:01 |
| 26 | 0.630828 | 0.694575 | 0.820739 | 00:01 |
| 27 | 0.615356 | 0.552568 | 0.864844 | 00:01 |
| 28 | 0.568692 | 0.501551 | 0.880125 | 00:01 |
| 29 | 0.540034 | 0.509745 | 0.874473 | 00:01 |
| 30 | 0.527858 | 0.512939 | 0.874526 | 00:01 |
| 31 | 0.515870 | 0.484364 | 0.884020 | 00:01 |
| 32 | 0.507249 | 0.541152 | 0.867288 | 00:01 |
| 33 | 0.638346 | 0.613729 | 0.848680 | 00:01 |
| 34 | 0.580912 | 0.475078 | 0.887630 | 00:01 |
| 35 | 0.527978 | 0.454268 | 0.892714 | 00:01 |
| 36 | 0.495153 | 0.442627 | 0.891738 | 00:01 |
| 37 | 0.487150 | 0.545637 | 0.869650 | 00:01 |
| 38 | 0.488512 | 0.449870 | 0.892205 | 00:01 |
| 39 | 0.480227 | 0.464473 | 0.889341 | 00:01 |
| 40 | 0.471190 | 0.448856 | 0.891767 | 00:01 |
| 41 | 0.491968 | 0.475216 | 0.884843 | 00:01 |
| 42 | 0.470660 | 0.450120 | 0.894087 | 00:01 |
| 43 | 0.480326 | 0.446868 | 0.890927 | 00:01 |
| 44 | 0.469917 | 0.483589 | 0.882842 | 00:00 |
| 45 | 0.608710 | 0.709529 | 0.833167 | 00:01 |
| 46 | 0.567673 | 0.460912 | 0.889897 | 00:01 |
| 47 | 0.514584 | 0.430129 | 0.897491 | 00:01 |
| 48 | 0.478464 | 0.414917 | 0.902172 | 00:01 |
| 49 | 0.459217 | 0.436341 | 0.894567 | 00:01 |
| 50 | 0.463525 | 0.451432 | 0.891992 | 00:01 |
| 51 | 0.468374 | 0.460046 | 0.889293 | 00:00 |
| 52 | 0.448112 | 0.419183 | 0.898976 | 00:00 |
| 53 | 0.642230 | 0.570725 | 0.862512 | 00:00 |
| 54 | 0.562047 | 0.430542 | 0.898763 | 00:00 |
| 55 | 0.498270 | 0.417436 | 0.900787 | 00:00 |
| 56 | 0.471800 | 0.413294 | 0.901711 | 00:00 |
| 57 | 0.458352 | 0.418333 | 0.900503 | 00:00 |
| 58 | 0.457185 | 0.466493 | 0.884683 | 00:01 |
| 59 | 0.452458 | 0.448444 | 0.889962 | 00:00 |
| 60 | 0.520453 | 0.444536 | 0.893608 | 00:01 |
| 61 | 0.476454 | 0.404115 | 0.903823 | 00:01 |
| 62 | 0.456225 | 0.478976 | 0.883055 | 00:01 |
| 63 | 0.449664 | 0.412289 | 0.900118 | 00:00 |
| 64 | 0.430974 | 0.401453 | 0.903764 | 00:01 |
| 65 | 0.419014 | 0.447311 | 0.892205 | 00:01 |
| 66 | 0.472171 | 0.499653 | 0.874101 | 00:01 |
| 67 | 0.447258 | 0.388832 | 0.907937 | 00:01 |
| 68 | 0.430973 | 0.452804 | 0.889110 | 00:01 |
| 69 | 0.467316 | 0.407934 | 0.903468 | 00:00 |
| 70 | 0.434231 | 0.381484 | 0.909440 | 00:00 |
| 71 | 0.412135 | 0.410772 | 0.903303 | 00:00 |
| 72 | 0.408759 | 0.488867 | 0.887571 | 00:00 |
| 73 | 0.490724 | 0.463815 | 0.886328 | 00:00 |
| 74 | 0.455707 | 0.383984 | 0.909055 | 00:01 |
| 75 | 0.427714 | 0.384917 | 0.909339 | 00:01 |
| 76 | 0.415478 | 0.428324 | 0.900012 | 00:01 |
| 77 | 0.436428 | 0.435506 | 0.893608 | 00:01 |
| 78 | 0.417452 | 0.372621 | 0.910701 | 00:01 |
| 79 | 0.401064 | 0.368297 | 0.913429 | 00:01 |
| 80 | 0.400733 | 0.376786 | 0.909624 | 00:01 |
| 81 | 0.395619 | 0.367422 | 0.913630 | 00:01 |
| 82 | 0.394352 | 0.387138 | 0.907067 | 00:01 |
| 83 | 0.394511 | 0.355445 | 0.916365 | 00:00 |
| 84 | 0.385621 | 0.378354 | 0.909452 | 00:01 |
| 85 | 0.385132 | 0.390068 | 0.903835 | 00:01 |
| 86 | 0.396378 | 0.408272 | 0.899651 | 00:01 |
| 87 | 0.400367 | 0.381332 | 0.909594 | 00:01 |
| 88 | 0.383582 | 0.342852 | 0.921135 | 00:01 |
| 89 | 0.364858 | 0.335672 | 0.923597 | 00:00 |
| 90 | 0.353290 | 0.344529 | 0.919087 | 00:01 |
| 91 | 0.383638 | 0.400630 | 0.903184 | 00:01 |
| 92 | 0.377436 | 0.344832 | 0.919910 | 00:01 |
| 93 | 0.372544 | 0.349291 | 0.918543 | 00:01 |
| 94 | 0.358517 | 0.340466 | 0.920828 | 00:01 |
| 95 | 0.368841 | 0.397364 | 0.906682 | 00:00 |
| 96 | 0.374042 | 0.365011 | 0.912630 | 00:01 |
| 97 | 0.366347 | 0.332969 | 0.924041 | 00:01 |
| 98 | 0.355495 | 0.333865 | 0.923544 | 00:00 |
| 99 | 0.348586 | 0.339383 | 0.921011 | 00:00 |
| 100 | 0.347919 | 0.341842 | 0.919490 | 00:00 |
| 101 | 0.347382 | 0.335846 | 0.922413 | 00:01 |
| 102 | 0.362783 | 0.361967 | 0.914003 | 00:01 |
| 103 | 0.356652 | 0.331301 | 0.923467 | 00:00 |
| 104 | 0.341300 | 0.316214 | 0.928959 | 00:00 |
| 105 | 0.336268 | 0.323315 | 0.926497 | 00:00 |
| 106 | 0.331520 | 0.319777 | 0.927770 | 00:00 |
| 107 | 0.337450 | 0.363321 | 0.914424 | 00:00 |
| 108 | 0.350334 | 0.339564 | 0.921437 | 00:00 |
| 109 | 0.341845 | 0.322457 | 0.926971 | 00:01 |
| 110 | 0.336658 | 0.320611 | 0.926598 | 00:01 |
| 111 | 0.327631 | 0.306033 | 0.931925 | 00:01 |
| 112 | 0.324505 | 0.342075 | 0.924467 | 00:01 |
| 113 | 0.327436 | 0.318198 | 0.928350 | 00:01 |
| 114 | 0.322787 | 0.311445 | 0.930048 | 00:01 |
| 115 | 0.344156 | 0.358735 | 0.914796 | 00:01 |
| 116 | 0.336370 | 0.307283 | 0.932268 | 00:01 |
| 117 | 0.323506 | 0.304596 | 0.932191 | 00:01 |
| 118 | 0.317701 | 0.300929 | 0.934612 | 00:01 |
| 119 | 0.313280 | 0.297949 | 0.935216 | 00:01 |
| 120 | 0.310268 | 0.307427 | 0.931463 | 00:01 |
| 121 | 0.309458 | 0.310683 | 0.930268 | 00:01 |
| 122 | 0.384177 | 0.398645 | 0.906499 | 00:01 |
| 123 | 0.363158 | 0.310172 | 0.931860 | 00:01 |
| 124 | 0.336900 | 0.305574 | 0.934061 | 00:01 |
| 125 | 0.323046 | 0.296804 | 0.936843 | 00:01 |
| 126 | 0.311328 | 0.291779 | 0.938299 | 00:01 |
| 127 | 0.302914 | 0.292930 | 0.937192 | 00:01 |
| 128 | 0.298936 | 0.290398 | 0.938246 | 00:01 |
| 129 | 0.298015 | 0.294183 | 0.936358 | 00:01 |
| 130 | 0.301172 | 0.297195 | 0.934641 | 00:01 |
| 131 | 0.300455 | 0.293688 | 0.936269 | 00:01 |
| 132 | 0.301193 | 0.294941 | 0.935754 | 00:01 |
| 133 | 0.298949 | 0.290869 | 0.937003 | 00:01 |
| 134 | 0.298088 | 0.292243 | 0.936967 | 00:01 |
| 135 | 0.299533 | 0.302599 | 0.933055 | 00:01 |
| 136 | 0.303822 | 0.296039 | 0.935085 | 00:01 |
| 137 | 0.303027 | 0.292551 | 0.936376 | 00:01 |
| 138 | 0.297278 | 0.284076 | 0.939903 | 00:01 |
| 139 | 0.293760 | 0.286506 | 0.938920 | 00:01 |
| 140 | 0.291565 | 0.282434 | 0.940388 | 00:01 |
| 141 | 0.290705 | 0.287238 | 0.938447 | 00:01 |
| 142 | 0.289410 | 0.282313 | 0.939832 | 00:01 |
| 143 | 0.289113 | 0.283686 | 0.940205 | 00:01 |
| 144 | 0.287541 | 0.281837 | 0.940868 | 00:01 |
| 145 | 0.288593 | 0.285768 | 0.938808 | 00:01 |
| 146 | 0.287050 | 0.281169 | 0.941028 | 00:01 |
| 147 | 0.285506 | 0.281760 | 0.940128 | 00:01 |
| 148 | 0.285783 | 0.283433 | 0.939406 | 00:01 |
| 149 | 0.285429 | 0.278858 | 0.941992 | 00:01 |
| 150 | 0.283137 | 0.278100 | 0.942229 | 00:01 |
| 151 | 0.281893 | 0.278660 | 0.941560 | 00:01 |
| 152 | 0.282925 | 0.281565 | 0.940743 | 00:01 |
| 153 | 0.284012 | 0.279491 | 0.941211 | 00:01 |
| 154 | 0.282136 | 0.277805 | 0.941714 | 00:01 |
| 155 | 0.279395 | 0.273553 | 0.943945 | 00:01 |
| 156 | 0.277198 | 0.272652 | 0.944336 | 00:01 |
| 157 | 0.276214 | 0.275451 | 0.942821 | 00:01 |
| 158 | 0.276345 | 0.273574 | 0.943235 | 00:01 |
| 159 | 0.275484 | 0.272749 | 0.944602 | 00:01 |
| 160 | 0.275380 | 0.274123 | 0.943353 | 00:01 |
| 161 | 0.274728 | 0.270259 | 0.945230 | 00:01 |
| 162 | 0.273720 | 0.272011 | 0.944460 | 00:01 |
| 163 | 0.272516 | 0.270527 | 0.945419 | 00:01 |
| 164 | 0.271829 | 0.269925 | 0.945301 | 00:01 |
| 165 | 0.271095 | 0.270437 | 0.945508 | 00:01 |
| 166 | 0.270988 | 0.269876 | 0.945005 | 00:01 |
| 167 | 0.270572 | 0.268732 | 0.945999 | 00:01 |
| 168 | 0.270086 | 0.268094 | 0.946182 | 00:01 |
| 169 | 0.269361 | 0.267193 | 0.946751 | 00:01 |
| 170 | 0.269130 | 0.267513 | 0.946265 | 00:01 |
| 171 | 0.268558 | 0.266665 | 0.946869 | 00:01 |
| 172 | 0.268545 | 0.267306 | 0.946615 | 00:01 |
| 173 | 0.267921 | 0.267159 | 0.946313 | 00:01 |
| 174 | 0.267430 | 0.267400 | 0.947112 | 00:01 |
| 175 | 0.267353 | 0.267791 | 0.946774 | 00:01 |
| 176 | 0.266944 | 0.266150 | 0.947337 | 00:01 |
| 177 | 0.266300 | 0.265913 | 0.947461 | 00:01 |
| 178 | 0.266342 | 0.266259 | 0.946934 | 00:01 |
| 179 | 0.266139 | 0.264837 | 0.947721 | 00:01 |
| 180 | 0.264860 | 0.264283 | 0.948053 | 00:01 |
| 181 | 0.265056 | 0.264293 | 0.948053 | 00:01 |
| 182 | 0.264797 | 0.265251 | 0.947751 | 00:01 |
| 183 | 0.264892 | 0.265048 | 0.947656 | 00:01 |
| 184 | 0.264637 | 0.264979 | 0.947846 | 00:01 |
| 185 | 0.263912 | 0.263642 | 0.948473 | 00:01 |
| 186 | 0.264208 | 0.263990 | 0.948201 | 00:01 |
| 187 | 0.263611 | 0.263521 | 0.948396 | 00:01 |
| 188 | 0.263421 | 0.263204 | 0.948579 | 00:01 |
| 189 | 0.262918 | 0.262930 | 0.948491 | 00:01 |
| 190 | 0.263049 | 0.263701 | 0.948343 | 00:01 |
| 191 | 0.263108 | 0.263562 | 0.948343 | 00:01 |
| 192 | 0.263592 | 0.262764 | 0.948384 | 00:01 |
| 193 | 0.262928 | 0.263324 | 0.948698 | 00:01 |
| 194 | 0.262404 | 0.262950 | 0.948846 | 00:01 |
| 195 | 0.262710 | 0.262878 | 0.948887 | 00:01 |
| 196 | 0.262429 | 0.263130 | 0.948822 | 00:01 |
| 197 | 0.262432 | 0.262794 | 0.948929 | 00:01 |
| 198 | 0.262091 | 0.263258 | 0.948816 | 00:01 |
| 199 | 0.262178 | 0.262949 | 0.948769 | 00:01 |
| 200 | 0.262225 | 0.263108 | 0.948680 | 00:01 |
In [30]:
# epoch train_loss valid_loss accuracy
#1 6.533147 6.611041 0.070201
#100 0.397764 0.400868 0.915774
learn.recorder.plot_losses()
In [31]:
#epoch train_loss valid_loss accuracy
#200 0.390284 0.403612 0.915976
learn.recorder.plot_metrics()
In [32]:
preds = learn.get_preds()
preds
Out[32]:
[tensor([[[2.8479e-04, 1.8869e-04, 4.3575e-04, ..., 8.4788e-04,
2.6956e-04, 3.5801e-04],
[1.7085e-17, 1.1150e-14, 1.8210e-11, ..., 4.0638e-44,
2.2000e-28, 8.1448e-26],
[7.8851e-16, 2.7053e-11, 1.4436e-16, ..., 1.2025e-37,
6.8574e-24, 1.1375e-25],
...,
[1.8901e-12, 3.6819e-16, 4.7385e-19, ..., 3.4443e-20,
1.6986e-16, 2.8513e-11],
[9.4661e-09, 8.5445e-14, 1.1175e-16, ..., 6.2395e-15,
9.2918e-11, 1.5421e-10],
[4.7298e-11, 4.8956e-14, 6.2887e-12, ..., 4.3914e-18,
1.7530e-16, 6.7330e-12]],
[[3.6172e-04, 3.3068e-17, 8.0418e-15, ..., 3.2444e-24,
2.7267e-15, 5.0313e-16],
[2.1805e-02, 6.3079e-14, 2.4923e-11, ..., 8.6841e-13,
8.2163e-15, 2.8146e-11],
[3.7471e-02, 5.0946e-14, 3.8290e-08, ..., 1.5797e-22,
8.0887e-15, 2.6675e-18],
...,
[4.9356e-17, 1.3540e-15, 2.3281e-14, ..., 2.3009e-23,
1.6231e-20, 8.8961e-20],
[1.0800e-12, 9.8252e-16, 2.8415e-11, ..., 2.9500e-24,
2.4244e-15, 5.2620e-20],
[3.0381e-13, 8.9189e-14, 5.5556e-14, ..., 7.7678e-23,
8.5923e-18, 3.8642e-20]],
[[2.8113e-05, 1.1214e-13, 1.9222e-08, ..., 1.3067e-22,
3.3412e-16, 4.1342e-19],
[9.9345e-07, 6.4545e-14, 4.5189e-14, ..., 7.3972e-24,
1.4223e-16, 3.8546e-16],
[4.9144e-06, 1.7640e-16, 3.6300e-13, ..., 1.8892e-24,
1.9568e-15, 4.5422e-20],
...,
[7.8641e-07, 2.4779e-18, 1.6241e-13, ..., 8.4795e-23,
6.1372e-18, 2.2027e-16],
[1.9368e-01, 3.4251e-13, 2.4147e-14, ..., 1.3825e-17,
1.9598e-13, 1.0961e-17],
[2.8557e-04, 2.0118e-04, 4.2282e-04, ..., 1.0479e-03,
3.7418e-04, 4.2860e-04]],
...,
[[3.1594e-10, 2.5360e-15, 8.2831e-16, ..., 8.5042e-19,
1.0322e-16, 2.8472e-12],
[2.0106e-09, 1.8444e-14, 1.6972e-12, ..., 8.3508e-17,
1.7839e-15, 5.3716e-12],
[5.2737e-09, 1.3271e-11, 1.8439e-15, ..., 5.1572e-17,
7.8568e-18, 5.4577e-12],
...,
[2.8509e-19, 4.7372e-21, 2.6799e-26, ..., 6.0342e-26,
3.5648e-23, 2.3521e-20],
[5.1526e-15, 1.9939e-17, 5.6215e-22, ..., 1.9845e-20,
1.9624e-23, 8.7569e-20],
[1.4218e-15, 4.1651e-18, 1.7305e-19, ..., 7.8990e-20,
1.4646e-22, 2.5532e-23]],
[[3.7014e-08, 1.0103e-11, 3.1732e-12, ..., 6.2884e-13,
4.0975e-10, 1.8280e-13],
[6.7632e-09, 2.0421e-13, 1.0572e-11, ..., 4.7765e-15,
9.8374e-13, 4.8582e-14],
[6.3062e-10, 2.5786e-12, 2.3445e-12, ..., 8.5418e-14,
1.6164e-13, 1.5980e-13],
...,
[3.8690e-08, 6.1642e-14, 1.1585e-15, ..., 2.1993e-17,
4.4787e-17, 9.5281e-19],
[1.3778e-07, 1.0438e-14, 4.0264e-16, ..., 6.6913e-18,
5.2772e-17, 6.9405e-18],
[1.0039e-06, 8.0889e-16, 7.2880e-16, ..., 2.0680e-17,
3.7356e-15, 3.4826e-18]],
[[3.9247e-12, 2.1604e-14, 6.1315e-12, ..., 1.5716e-19,
1.0281e-17, 2.3410e-13],
[1.6816e-11, 5.5593e-16, 7.4606e-15, ..., 1.6211e-16,
1.5126e-17, 4.5233e-13],
[4.6010e-10, 1.7874e-15, 3.8081e-11, ..., 2.1024e-16,
1.2333e-11, 8.1597e-11],
...,
[3.2753e-01, 3.3656e-10, 9.3145e-12, ..., 5.6315e-13,
1.1290e-10, 4.9703e-16],
[2.4188e-04, 2.0839e-04, 3.9753e-04, ..., 9.8902e-04,
3.8965e-04, 4.4383e-04],
[9.4469e-18, 4.5557e-14, 1.6877e-11, ..., 3.1389e-43,
2.9588e-28, 1.2444e-25]]]),
tensor([[ 14, 628, 1310, ..., 341, 650, 946],
[1326, 1417, 1460, ..., 260, 1498, 1701],
[ 135, 766, 1460, ..., 1179, 0, 900],
...,
[1090, 608, 117, ..., 452, 1556, 1537],
[ 256, 560, 1255, ..., 353, 1499, 598],
[ 946, 1088, 635, ..., 0, 14, 628]])]
Load a RNN model already trained¶
Call the function from the jupyter notebook current folder:
- learn.load(datapath4file(URLs.LOCAL_PATH/'models'/'name.gru.model.you.want.to.load'))
You need to run the class Model_Density(nn.Module) cells ABOVE first!
In [30]:
m = learn.model
#set batch size to 1
learn.model.bs=1
#turn off dropout
m.eval()
#reset hidden state
m.reset()
idxs = learn.data.train_ds[150][0].data
idxs = idxs[None,:]
print(idxs, idxs.shape)
p = m.forward(torch.tensor(idxs, device=device))
cls = F.softmax(p[0], dim=1).argmax(dim=1)
cls
[[ 0 869 1592 372 143 1472 14 628 1310 756 1294 629 1691 1282 478 766 1460 1179]] (1, 18)
Out[30]:
tensor([ 210, 1592, 372, 143, 1472, 14, 628, 1310, 756, 1294, 629, 1691,
1282, 478, 766, 1460, 1179, 775], device='cuda:0')
In [31]:
m = learn.model
#set batch size to 1
learn.model.bs=1
#turn off dropout
m.eval()
#reset hidden state
m.reset()
idxs = np.array([0, 869, 1592, 372, 143, 1472])
idxs = idxs[None,:]
p = m.forward(torch.tensor(idxs, device=device))
cls = F.softmax(p[0], dim=1).argmax(dim=1)
cls
Out[31]:
tensor([ 210, 1592, 372, 143, 1472, 14], device='cuda:0')
In [32]:
m.i_h(torch.tensor(idxs, device=device))
Out[32]:
tensor([[[10.0000, 10.0000],
[ 1.2430, 1.6694],
[ 1.2245, 1.6757],
[ 1.2272, 1.6428],
[ 1.2363, 1.6086],
[ 1.2392, 1.5499]]], device='cuda:0')
In [33]:
m = learn.model
#set batch size to 1
learn.model.bs=1
#turn off dropout
m.eval()
#reset hidden state
m.reset()
p = m.forward_with_embedded(torch.tensor([[[10.0000, 10.0000],
[ 1.2430, 1.6694],
[ 1.2245, 1.6757],
[ 1.2272, 1.6428],
[ 1.2363, 1.6086],
[ 1.2392, 1.5499]]], device=device))
cls = F.softmax(p[0], dim=1).argmax(dim=1)
cls
Out[33]:
tensor([ 210, 1592, 372, 143, 1472, 14], device='cuda:0')
In [34]:
sorted([ridx+1 for ridx in hqshift._idx_root])
Out[34]:
[494, 786, 969, 1179, 1480, 1616]
And test the RNN output with some rounded inputs:
In [36]:
from sklearn.neighbors import NearestNeighbors
import time
fig, ax = plt.subplots(figsize=fig_size)
t0 = time.time()
sns.kdeplot(test_data[:,0],test_data[:,1],
gridsize=256, n_levels=64, bw=hqshift._emst.bandwidth,
ax=ax, shade_lowest=False, linestyles='--', linewidths=0.5)
print(time.time()-t0)
nbrs = NearestNeighbors(n_neighbors=1, algorithm='ball_tree').fit(test_data)
query_points = [[-0.200, 0.2000], [-0.300, -0.100], [1.0000, 1.2000], [1.3000, 1.4000],
[1.0000, 1.8000], [-2.000, 1.0000], [-1.200, 1.8000], [-1.200, -1.000],
[1.2000, -1.000], [1.2000, -0.800], [1.0000, -0.500], [-1.200, -0.500],
[-2.000, 1.4000], [-1.000, 0.4000], [-2.000, 2.0000], [-0.800, -1.100],
[1.6000, -0.500], [1.5500, -1.000], [-0.800, -0.300], [-1.250, 0.0000],
[0.0000, -1.300], [0.3000, -1.300], [0.0000, -0.500], [0.7000, -0.800],
[0.6000, -0.400], [0.6000, 1.5000], [-1.300, 1.6000], [1.0000, 1.7500],
[0.3500, -1.000], [-0.800, -0.500], [1.2000, 1.9000], [1.2000, -0.700],
[0.5000, -0.100], [0.0000, 0.5000], [1.2000, -0.700], [0.5000, 1.0000],
[0.8000, -1.400], [1.5000, -1.400], [0.5000, 1.5000], [1.0000, 0.0000]]
plt.setp(plt.gca().get_xticklabels(), visible=False)
plt.setp(plt.gca().get_yticklabels(), visible=False)
plt.tight_layout()
distances, indices = nbrs.kneighbors(query_points)
#_idx_root := [494, 786, 969, 1179, 1480, 1616]
for idx in hqshift._idx_root:
plt.plot((test_data[idx][0]),
(test_data[idx][1]), 'X', color='k', markersize=16)
for q_p in indices:
m = learn.model
#set batch size to 1
learn.model.bs=1
#turn off dropout
m.eval()
#reset hidden state
m.reset()
idxs = np.array([0, q_p[0]+1])
idxs = torch.tensor(idxs, device=device)
for _ in np.arange(bptt):
p = m.forward(idxs[None,:])
cls = F.softmax(p[0], dim=1).argmax(dim=1)
if cls[-1].item()==0: print(0); break
idxs = torch.cat((idxs, cls[-1].unsqueeze(0)))
if cls[-1].item() in ([ridx+1 for ridx in hqshift._idx_root]): break
# print(idxs)
# import pdb;pdb.set_trace()
plt.plot(*test_data[idxs[1:]-1][0], 'o', color='purple', markersize=10)
plt.plot(*test_data[idxs[1:]-1].T, color='purple', linewidth=2)
curaxes = plt.gca()
curaxes.set_xlim(ax_xlim)
curaxes.set_ylim(ax_ylim)
fig.savefig('rnn_out.png', dpi=200, bbox_inches='tight')
9.184154033660889
Sklearn Benchmarks¶
Synthetic Dataset Comparison: HQuickShift settings are min cluster size = 30, min mode size = 50, min sample = 1 and the bandwidth is estimated with EMST of HQuickShift. Quickshift++ settings are fixed at k = 20, β = 0.7 as suggested by authors. For DBSCAN we use the settings given in sklearn clustering benchmark.The bandwidth for QuickShift is estimated with estimate bandwidth routine (parameter quantile = .005) of sklearn.cluster module and tuned further to obtain a reasonable number of clusters for all datasets.
In [26]:
%reload_ext autoreload
%autoreload 2
%matplotlib inline
import warnings
import numpy as np
import matplotlib.pyplot as plt
from sklearn import cluster, datasets, mixture
from sklearn.neighbors import kneighbors_graph
from sklearn.preprocessing import StandardScaler
from itertools import cycle, islice
from QuickshiftPP import *
import hdbscan
np.random.seed(0)
# ============
# Generate datasets. We choose the size big enough to see the scalability
# of the algorithms, but not too big to avoid too long running times
# ============
n_samples = 1500
noisy_circles = datasets.make_circles(n_samples=n_samples, factor=.5,
noise=.05)
noisy_moons = datasets.make_moons(n_samples=n_samples, noise=.05)
blobs = datasets.make_blobs(n_samples=n_samples, random_state=8)
no_structure = np.random.rand(n_samples, 2), None
# Anisotropicly distributed data
random_state = 170
X, y = datasets.make_blobs(n_samples=n_samples, random_state=random_state)
transformation = [[0.6, -0.6], [-0.4, 0.8]]
X_aniso = np.dot(X, transformation)
aniso = (X_aniso, y)
# blobs with varied variances
varied = datasets.make_blobs(n_samples=n_samples,
cluster_std=[1.0, 2.5, 0.5],
random_state=random_state)
# ============
# Set up cluster parameters
# ============
plt.figure(figsize=fig_size)
plt.subplots_adjust(left=.02, right=.98, bottom=.001, top=.96, wspace=.05,
hspace=.01)
plot_num = 1
default_base = {'quantile': .005,
'eps': .3,
'damping': .9,
'preference': -200,
'n_neighbors': 10,
'n_clusters': 3,
'min_samples': 20,
'xi': 0.05,
'min_cluster_size': 0.1}
datasets = [
(noisy_circles, {'damping': .77, 'preference': -240,
'quantile': .2, 'n_clusters': 2,
'min_samples': 20, 'xi': 0.25}),
(noisy_moons, {'damping': .75, 'preference': -220, 'n_clusters': 2}),
(varied, {'eps': .18, 'n_neighbors': 2,
'min_samples': 5, 'xi': 0.035, 'min_cluster_size': .2}),
(aniso, {'eps': .15, 'n_neighbors': 2,
'min_samples': 20, 'xi': 0.1, 'min_cluster_size': .2}),
(blobs, {}),
(no_structure, {})]
for i_dataset, (dataset, algo_params) in enumerate(datasets):
# update parameters with dataset-specific values
params = default_base.copy()
params.update(algo_params)
X, y = dataset
# normalize dataset for easier parameter selection
X = StandardScaler().fit_transform(X)
# estimate bandwidth for mean shift
bandwidth = cluster.estimate_bandwidth(X, quantile=params['quantile'])
# connectivity matrix for structured Ward
connectivity = kneighbors_graph(
X, n_neighbors=params['n_neighbors'], include_self=False)
# make connectivity symmetric
connectivity = 0.5 * (connectivity + connectivity.T)
# ============
# Create cluster objects
# ============
hqshift = HQSHIFT(min_cluster_size=30, min_mode_size=50, min_samples=1, allow_single_cluster=True)
quickshiftpp = QuickshiftPP(k=20, beta=.7)
dbscan = cluster.DBSCAN(eps=params['eps'])
quick_s_norm = QuickShift(window_type="normal", bandwidth=bandwidth*0.85)
clustering_algorithms = (
('HQuickShift', hqshift),
('Quickshift++', quickshiftpp),
('DBSCAN', dbscan),
('QuickShift', quick_s_norm),)
for name, algorithm in clustering_algorithms:
# catch warnings related to kneighbors_graph
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore",
message="the number of connected components of the " +
"connectivity matrix is [0-9]{1,2}" +
" > 1. Completing it to avoid stopping the tree early.",
category=UserWarning)
warnings.filterwarnings(
"ignore",
message="Graph is not fully connected, spectral embedding" +
" may not work as expected.",
category=UserWarning)
algorithm.fit(X)
if hasattr(algorithm, 'labels_'):
y_pred = algorithm.labels_.astype(np.int)
elif hasattr(algorithm, 'memberships'):
y_pred = algorithm.memberships
else:
y_pred = algorithm.predict(X)
plt.subplot(len(datasets), len(clustering_algorithms), plot_num)
if i_dataset == 0:
plt.title(name, size=28)
colors = np.array(list(islice(cycle(['#377eb8', '#ff7f00', '#4daf4a',
'#f781bf', '#a65628', '#984ea3',
'#999999', '#e41a1c', '#dede00']),
int(max(y_pred) + 1))))
# add black color for outliers (if any)
colors = np.append(colors, ["#000000"])
plt.scatter(X[:, 0], X[:, 1], s=10, color=colors[y_pred])
plt.xlim(-2.5, 2.5)
plt.ylim(-2.5, 2.5)
plt.xticks(())
plt.yticks(())
plot_num += 1
plt.savefig('benchmark_sklearn.png', dpi=200, bbox_inches='tight')
plt.show()
Real Dataset Benchmarks¶
In [30]:
#%reload_ext memory_profiler
from sklearn.cluster import KMeans
from sklearn.metrics import adjusted_rand_score, adjusted_mutual_info_score
from sklearn.decomposition import PCA
from sklearn.preprocessing import scale
import math
import umap
import time
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
seed = 0
seed_lst = [0, 23, 42, 1234, 43210, 1133557799, 22446688, 123456789, 987654321, 86420]
random_seed(seed_value=seed, use_cuda=use_cuda)
Collect and Plot Batch Run Results¶
The *.pkl files contained results of the experiments are produced with parameter_select.py script located under data_sets\XX_bunch folders. First run those scripts to produce the results and then you can collect and plot the results here:
In [28]:
import pickle
datasets = ['banknote', 'glass', 'iris', 'mnist', 'seeds', 'phoneme', 'page-blocks']
methods = ['kmeans', 'qshift', 'hdbscan', 'qshiftpp']
rowidx = {ds:i for i,ds in enumerate(datasets)}
colidx = {mt:i for i,mt in enumerate(methods)}
fig1, axes1 = plt.subplots(nrows=len(datasets), ncols=len(methods)+2, sharey="row", figsize=fig_size)
plt.subplots_adjust(left=.02, right=.98, bottom=.001, top=.96, wspace=.05, hspace=.01)
for filename in glob.iglob('data_sets/**/*.pkl', recursive=True):
# print(filename)
fname = os.path.splitext(os.path.basename(filename))[0]
# print(fname)
fname_parts = fname.split('_')
dataset = fname_parts[0]
method = fname_parts[1]
ri, ci = rowidx[dataset], colidx[method]
if len(fname_parts) == 2:
if ri == 0:
axes1[ri, ci].set_title(method, size=24)
if ci == 0:
axes1[ri, ci].set_ylabel(dataset, size=24)
with open(filename, 'rb') as input_file:
datadict = pickle.load(input_file)
if method == 'kmeans':
axes1[ri, ci].plot(datadict['k'], datadict['ami'], 'b.-', label='AMI')
axes1[ri, ci].plot(datadict['k'], datadict['ari'], 'r.-', label='ARI')
axes1[ri, ci].grid()
if ri == 0 and ci == 0: axes1[ri, ci].legend(loc='upper left')
elif method == 'qshift':
axes1[ri, ci].plot(datadict['bw'], datadict['ami'], 'b.-',
datadict['bw'], datadict['ari'], 'r.-')
axes1[ri, ci].grid()
elif method == 'hdbscan':
if ri == 0: axes1[ri, ci].set_title('hqshift[min_clust]', size=20)
best_idx_ari = np.argmax(datadict['ari'])
best_idx_nmi = np.argmax(datadict['ami'])
best_min_sample_size = datadict['k'][best_idx_nmi]
best_k_idx = np.where(np.array(datadict['k'])==best_min_sample_size)[0]
axes1[ri, ci].plot(np.array(datadict['minclst'])[best_k_idx],
np.array(datadict['ami'])[best_k_idx], 'b.-',
np.array(datadict['minclst'])[best_k_idx],
np.array(datadict['ari'])[best_k_idx], 'r.-')
axes1[ri, ci].grid()
print(dataset, method, best_min_sample_size)
if ri == 0: axes1[ri, ci+1].set_title('hqshift[min_sample]', size=20)
best_minclst = datadict['minclst'][best_idx_nmi]
best_minclst_idx = np.where(np.array(datadict['minclst'])==best_minclst)[0]
axes1[ri, ci+1].plot(np.array(datadict['k'])[best_minclst_idx],
np.array(datadict['ami'])[best_minclst_idx], 'b.-',
np.array(datadict['k'])[best_minclst_idx],
np.array(datadict['ari'])[best_minclst_idx], 'r.-')
axes1[ri, ci+1].grid()
print(dataset, method, best_minclst)
else:
if ri == 0: axes1[ri, ci+1].set_title('qshift++[beta]', size=24)
best_idx_ari = np.argmax(datadict['ari'])
best_idx_nmi = np.argmax(datadict['ami'])
best_k_idx = np.where(np.array(datadict['k'])==datadict['k'][best_idx_nmi])[0]
axes1[ri, ci+1].plot(np.array(datadict['b'])[best_k_idx],
np.array(datadict['ami'])[best_k_idx], 'b.-',
np.array(datadict['b'])[best_k_idx],
np.array(datadict['ari'])[best_k_idx], 'r.-')
axes1[ri, ci+1].grid()
print(dataset, method, datadict['k'][best_idx_nmi])
if ri == 0: axes1[ri, ci+2].set_title('qshift++[k]', size=24)
best_idx_ari = np.argmax(datadict['ari'])
best_idx_nmi = np.argmax(datadict['ami'])
best_b_idx = np.where(np.array(datadict['b'])==datadict['b'][best_idx_nmi])[0]
axes1[ri, ci+2].plot(np.array(datadict['k'])[best_b_idx],
np.array(datadict['ami'])[best_b_idx], 'b.-',
np.array(datadict['k'])[best_b_idx],
np.array(datadict['ari'])[best_b_idx], 'r.-')
axes1[ri, ci+2].grid()
print(dataset, method, datadict['b'][best_idx_nmi])
plt.tight_layout()
plt.savefig('experiment_1.png', dpi=200, bbox_inches='tight')
seeds qshiftpp 19 seeds qshiftpp 0.6 iris qshiftpp 18 iris qshiftpp 0.1 banknote qshiftpp 66 banknote qshiftpp 0.6 mnist hdbscan 6 mnist hdbscan 15 seeds hdbscan 43 seeds hdbscan 15 mnist qshiftpp 77 mnist qshiftpp 0.30000000000000004 banknote hdbscan 2 banknote hdbscan 114 glass qshiftpp 28 glass qshiftpp 0.4 iris hdbscan 4 iris hdbscan 15 glass hdbscan 2 glass hdbscan 11 page-blocks hdbscan 19 page-blocks hdbscan 15 page-blocks qshiftpp 260 page-blocks qshiftpp 0.6 phoneme hdbscan 71 phoneme hdbscan 15 phoneme qshiftpp 46 phoneme qshiftpp 0.8
In [64]:
fig2, axes2 = plt.subplots(nrows=len(datasets), ncols=len(methods), sharey="row", figsize=fig_size)
plt.subplots_adjust(left=.02, right=.98, bottom=.001, top=.96, wspace=.05, hspace=.01)
for filename in glob.iglob('data_sets/**/*.pkl', recursive=True):
# print(filename)
fname = os.path.splitext(os.path.basename(filename))[0]
# print(fname)
fname_parts = fname.split('_')
dataset = fname_parts[0]
method = fname_parts[1]
ri, ci = rowidx[dataset], colidx[method]
if len(fname_parts) > 2:
if ci == 0:
axes2[ri, ci].set_ylabel(dataset, size=24)
with open(filename, 'rb') as input_file:
datadict = pickle.load(input_file)
t = np.arange(1, 11)[1:]
axes2[ri, ci].plot(t, datadict['ami'][1:], 'b.-', label='AMI')
axes2[ri, ci].plot(t, datadict['ari'][1:], 'r.-', label='ARI')
axes2[ri, ci].tick_params('y', colors='b')
#axes2[ri, ci].set_xlabel('Run [index]')
ax2 = axes2[ri, ci].twinx()
ax2.plot(t, datadict['n_clusters'][1:], 'g.-', label='N_CLUST')
ax2.tick_params('y', colors='g')
#ax2.set_ylabel('number of clusters', color='g')
if method == 'kmeans':
if ri == 0: axes2[ri, ci].set_title(method, size=24)
if ri == 0 and ci == 0:
axes2[ri, ci].legend(loc='upper left')
ax2.legend(loc='upper right')
elif method == 'qshift':
if ri == 0: axes2[ri, ci].set_title('qshift', size=24)
elif method == 'hdbscan':
if ri == 0: axes2[ri, ci].set_title('hqshift', size=24)
else:
if ri == 0: axes2[ri, ci].set_title('qshift++', size=24)
axes2[ri, ci].grid()
plt.tight_layout()
plt.savefig('experiment_2.png', dpi=200, bbox_inches='tight')
Quick Test¶
In this section, we perform some tests with real datasets to demonstrate their performance:
In [60]:
#data0 = pd.read_csv("data_sets/00_bunch/banknote/banknote.csv", header=None)
#data0 = pd.read_csv("data_sets/00_bunch/glass/glass.csv", header=None)
#data0 = pd.read_csv("data_sets/00_bunch/iris/iris.csv", header=None)
#data0 = pd.read_csv("data_sets/00_bunch/mnist/mnist.csv", header=None)
#data0 = pd.read_csv("data_sets/00_bunch/seeds/seeds.csv", header=None)
data0 = pd.read_csv("data_sets/01_bunch/phoneme/phoneme.csv", header=None)
#data0 = pd.read_csv("data_sets/02_bunch/page-blocks/page-blocks.csv", header=None)
X = data0.iloc[:,:-1].values
y = data0.iloc[:, -1].values
In [61]:
X.shape, y.shape
Out[61]:
((4508, 256), (4508,))
In [62]:
num_classes = np.unique(y).shape[0]
num_samples = y.shape[0]
# find minority class label
num_samples_minority = math.inf
for lbl in np.unique(y):
curr_num_samples = y[y==lbl].shape[0]
if curr_num_samples < num_samples_minority:
num_samples_minority = curr_num_samples
min_label = lbl
print(min_label, num_classes, num_samples_minority, num_samples)
2 5 695 4508
In [63]:
if X.shape[1] > 4:
X = umap.UMAP(
n_neighbors=50,
min_dist=0.0,
n_components=4,
random_state=seed,
).fit_transform(X)
In [279]:
X.shape, y.shape
Out[279]:
((1372, 4), (1372,))
HDBSCAN¶
In [280]:
best_min_cluster_size = 17
if X.shape[0] > 1000:
best_min_cluster_size = 125
best_min_sample_size = 3
best_min_mode_size = best_min_cluster_size
In [281]:
clusterer = hdbscan.HDBSCAN(min_cluster_size=best_min_cluster_size, min_samples=best_min_sample_size)
y_hat = clusterer.fit_predict(X)
print("Adj. Rand Index Score: %f." % adjusted_rand_score(y_hat, y))
print("Adj. Mutual Info Score: %f." % adjusted_mutual_info_score(y_hat, y))
print('Classes: ', np.unique(y_hat), 'n_clusters: ', np.unique(y_hat)[-1]+1)
Adj. Rand Index Score: 0.792122. Adj. Mutual Info Score: 0.625601. Classes: [-1 0 1] n_clusters: 2
In [282]:
clustered = (y_hat >= 0)
(
adjusted_rand_score(y[clustered], y_hat[clustered]),
adjusted_mutual_info_score(y[clustered], y_hat[clustered])
)
Out[282]:
(0.9649402548773788, 0.9342344910570453)
In [283]:
y_hat[y_hat==-1].shape
Out[283]:
(127,)
Quickshift++¶
In [284]:
X_qspp = X.copy(order='C')
k=25
if X.shape[0] > 1000:
k = 60
model = QuickshiftPP(k=k, beta=.6)
# Compute the clustering.
model.fit(X_qspp)
y_hat = model.memberships
print("Adj. Rand Index Score: %f." % adjusted_rand_score(y_hat, y))
print("Adj. Mutual Info Score: %f." % adjusted_mutual_info_score(y_hat, y))
print('Classes: ', np.unique(y_hat), 'n_clusters: ', np.unique(y_hat)[-1]+1)
Adj. Rand Index Score: 0.690146. Adj. Mutual Info Score: 0.555389. Classes: [0 1 2 3] n_clusters: 4
HQSHIFT¶
In [285]:
hqshift = HQSHIFT(min_cluster_size=best_min_cluster_size,
min_mode_size=best_min_mode_size,
min_samples=best_min_sample_size,
allow_single_cluster=False).fit(X)
# cluster labels
y_hat = hqshift.labels_
print("Adj. Rand Index Score: %f." % adjusted_rand_score(y_hat, y))
print("Adj. Mutual Info Score: %f." % adjusted_mutual_info_score(y_hat, y))
print('Classes: ', np.unique(y_hat), 'n_clusters: ', np.unique(y_hat)[-1]+1)
Adj. Rand Index Score: 0.798521. Adj. Mutual Info Score: 0.632384. Classes: [-1 0 1] n_clusters: 2
In [286]:
clustered = (y_hat >= 0)
(
adjusted_rand_score(y[clustered], y_hat[clustered]),
adjusted_mutual_info_score(y[clustered], y_hat[clustered])
)
Out[286]:
(0.9650794905641422, 0.9344698677014239)
In [287]:
y_hat[y_hat==-1].shape
Out[287]:
(122,)
In [288]:
len(hqshift.paths), hqshift._idx_root
Out[288]:
(1372, [1334, 1002, 35, 180, 63, 1062])
In [289]:
clust_paths = [[j]+pth[:pth.index(idx)+1] for idx in hqshift._idx_root
for j, pth in enumerate(hqshift.paths) if idx in pth]
clust_data = [X[pth] for pth in clust_paths]
# Construct Vocabulary, make sure BOS (<xxbos>) idx is 0
vocab_tok = [BOS] + [str(idx) for idx in np.arange(X.shape[0])]
# => ['<pad>', '0', '1', '2', '3',... 'seq_len-1']
vocab_ = {idx:tok for idx,tok in enumerate(vocab_tok)}
ids = [[0]+list(np.array(pth)+1) for pth in clust_paths]
#ids[150], ids[-1], vocab_[ids[-1][-1]]
len(clust_paths)
Out[289]:
3058
In [290]:
nh=256
bs=128
bptt=20
vocab=Vocab(vocab_)
nv = len(vocab.itos); nv
Out[290]:
1373
In [291]:
class Model_Density(nn.Module):
def __init__(self, nv, nh, bs):
super().__init__()
self.nv = nv
self.nh = nh
self.bs = bs
self.i_h = nn.Embedding(self.nv,self.nh)
self.rnn = nn.GRU(self.nh, self.nh, 2, batch_first=True)
self.h_o = nn.Linear(self.nh, self.nv)
self.bn = BatchNorm1dFlat(self.nh)
self.reset()
def reset(self):
self.h = torch.zeros(2, self.bs, self.nh).cuda()
def forward(self, x):
res,h = self.rnn(self.i_h(x), self.h)
self.h = h.detach()
return self.h_o(self.bn(res))
In [292]:
ids = np.array(ids)
data = TextLMDataBunch.from_ids('/tmp', vocab=vocab,
train_ids=ids, valid_ids=ids,
bs=bs, bptt=bptt, device=device)
data
Out[292]:
TextLMDataBunch; Train: LabelList (3058 items) x: LMTextList xxbos 30 936 1058 1119 814 1029 1266 997 1334,xxbos 95 326 908 1030 1091 1152 847 786 969 915 834 1002 903 916 1215 849 922 1205 1266 997 1334,xxbos 326 908 1030 1091 1152 847 786 969 915 834 1002 903 916 1215 849 922 1205 1266 997 1334,xxbos 349 807 1226 1287 1165 860 799 1348 1104 921 1043 1113 930 846 968 785 1334,xxbos 764 825 1037 1139 773 1198 1137 1259 915 834 1002 903 916 1215 849 922 1205 1266 997 1334 y: LMLabelList ,,,, Path: /tmp; Valid: LabelList (3058 items) x: LMTextList xxbos 30 936 1058 1119 814 1029 1266 997 1334,xxbos 95 326 908 1030 1091 1152 847 786 969 915 834 1002 903 916 1215 849 922 1205 1266 997 1334,xxbos 326 908 1030 1091 1152 847 786 969 915 834 1002 903 916 1215 849 922 1205 1266 997 1334,xxbos 349 807 1226 1287 1165 860 799 1348 1104 921 1043 1113 930 846 968 785 1334,xxbos 764 825 1037 1139 773 1198 1137 1259 915 834 1002 903 916 1215 849 922 1205 1266 997 1334 y: LMLabelList ,,,, Path: /tmp; Test: None
In [293]:
random_seed(seed_value=seed, use_cuda=use_cuda)
learn = Learner(data, Model_Density(nv, nh, bs), metrics=accuracy)
In [294]:
learn.lr_find()
learn.recorder.plot()
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
In [295]:
learn.fit_one_cycle(50, 1e-2);
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 5.977040 | 6.072595 | 0.635326 | 00:00 |
| 2 | 4.842335 | 3.327955 | 0.767663 | 00:00 |
| 3 | 3.639819 | 1.595332 | 0.825628 | 00:00 |
| 4 | 2.564142 | 0.820289 | 0.877327 | 00:00 |
| 5 | 1.778812 | 0.529067 | 0.909358 | 00:00 |
| 6 | 1.267308 | 0.454194 | 0.918139 | 00:00 |
| 7 | 0.953756 | 0.460074 | 0.912670 | 00:00 |
| 8 | 0.762700 | 0.445945 | 0.916491 | 00:00 |
| 9 | 0.645771 | 0.451389 | 0.918716 | 00:00 |
| 10 | 0.572739 | 0.453099 | 0.918546 | 00:00 |
| 11 | 0.528556 | 0.458762 | 0.918614 | 00:00 |
| 12 | 0.500923 | 0.470596 | 0.918784 | 00:00 |
| 13 | 0.484658 | 0.464671 | 0.916474 | 00:00 |
| 14 | 0.473280 | 0.464757 | 0.918920 | 00:00 |
| 15 | 0.464973 | 0.453933 | 0.918920 | 00:00 |
| 16 | 0.457714 | 0.438242 | 0.918971 | 00:00 |
| 17 | 0.451527 | 0.434950 | 0.918240 | 00:00 |
| 18 | 0.446123 | 0.431196 | 0.918903 | 00:00 |
| 19 | 0.441447 | 0.428390 | 0.918801 | 00:00 |
| 20 | 0.436984 | 0.426108 | 0.918852 | 00:00 |
| 21 | 0.433461 | 0.423290 | 0.919395 | 00:00 |
| 22 | 0.430805 | 0.422857 | 0.918869 | 00:00 |
| 23 | 0.428794 | 0.421856 | 0.917714 | 00:00 |
| 24 | 0.427836 | 0.424104 | 0.919582 | 00:00 |
| 25 | 0.427204 | 0.421568 | 0.918869 | 00:00 |
| 26 | 0.426329 | 0.422266 | 0.918818 | 00:00 |
| 27 | 0.425386 | 0.422684 | 0.918903 | 00:00 |
| 28 | 0.425470 | 0.421338 | 0.918903 | 00:00 |
| 29 | 0.425313 | 0.421826 | 0.918835 | 00:00 |
| 30 | 0.424621 | 0.422219 | 0.918852 | 00:00 |
| 31 | 0.424393 | 0.421966 | 0.916593 | 00:00 |
| 32 | 0.424456 | 0.421035 | 0.918852 | 00:00 |
| 33 | 0.424005 | 0.420840 | 0.918767 | 00:00 |
| 34 | 0.423243 | 0.420765 | 0.918308 | 00:00 |
| 35 | 0.423248 | 0.420912 | 0.918869 | 00:00 |
| 36 | 0.423039 | 0.421797 | 0.918869 | 00:00 |
| 37 | 0.422846 | 0.420816 | 0.918852 | 00:00 |
| 38 | 0.422578 | 0.420531 | 0.918750 | 00:00 |
| 39 | 0.422143 | 0.420253 | 0.918920 | 00:00 |
| 40 | 0.422192 | 0.420137 | 0.918308 | 00:00 |
| 41 | 0.421710 | 0.420222 | 0.917188 | 00:00 |
| 42 | 0.421760 | 0.420095 | 0.918869 | 00:00 |
| 43 | 0.421348 | 0.420104 | 0.919158 | 00:00 |
| 44 | 0.421488 | 0.420145 | 0.918818 | 00:00 |
| 45 | 0.420881 | 0.420048 | 0.918920 | 00:00 |
| 46 | 0.420800 | 0.420038 | 0.918971 | 00:00 |
| 47 | 0.420852 | 0.420025 | 0.918835 | 00:00 |
| 48 | 0.420573 | 0.419994 | 0.918869 | 00:00 |
| 49 | 0.420338 | 0.419977 | 0.918597 | 00:00 |
| 50 | 0.420014 | 0.419995 | 0.918767 | 00:00 |
In [296]:
learn.recorder.plot_losses()
In [297]:
learn.recorder.plot_metrics()
In [298]:
# paths, number of samples
len(learn.data.train_ds), X.shape[0]
Out[298]:
(3058, 1372)
In [299]:
stop_cond = sorted([ridx+1 for ridx in hqshift._idx_root])
stop_cond
Out[299]:
[36, 64, 181, 1003, 1063, 1335]
In [300]:
y_hat = -2*np.ones(X.shape[0])
for q_p in range(X.shape[0]):
m = learn.model
#set batch size to 1
learn.model.bs=1
#turn off dropout
m.eval()
#reset hidden state
m.reset()
idxs = np.array([0, q_p+1])
idxs = torch.tensor(idxs, device=device)
# import pdb; pdb.set_trace()
for _ in range(bptt):
p = m.forward(idxs[None,:])
cls = F.softmax(p[0], dim=1).argmax(dim=1)
if cls[-1].item()==0: print(0); break
idxs = torch.cat((idxs, cls[-1].unsqueeze(0)))
if cls[-1].item() in stop_cond: break
#import pdb;pdb.set_trace()
#set the class label from the last index of the trajectory produced by HQuickShift
y_hat[q_p] = hqshift.labels_[idxs[-1]-1]
print(q_p, y_hat[q_p])
# import pdb; pdb.set_trace()
0 0.0 1 0.0 2 0.0 3 0.0 4 0.0 5 0.0 6 0.0 7 0.0 8 0.0 9 0.0 10 0.0 11 0.0 12 0.0 13 0.0 14 0.0 15 0.0 16 0.0 17 0.0 18 0.0 19 0.0 20 0.0 21 0.0 22 0.0 23 0.0 24 0.0 25 0.0 26 0.0 27 0.0 28 0.0 29 0.0 30 1.0 31 0.0 32 0.0 33 0.0 34 0.0 35 0.0 36 0.0 37 0.0 38 0.0 39 0.0 40 0.0 41 0.0 42 0.0 43 0.0 44 0.0 45 0.0 46 0.0 47 0.0 48 0.0 49 0.0 50 0.0 51 0.0 52 0.0 53 0.0 54 0.0 55 0.0 56 0.0 57 0.0 58 0.0 59 0.0 60 0.0 61 0.0 62 0.0 63 1.0 64 0.0 65 0.0 66 0.0 67 0.0 68 0.0 69 0.0 70 0.0 71 0.0 72 0.0 73 0.0 74 0.0 75 0.0 76 0.0 77 0.0 78 0.0 79 0.0 80 0.0 81 0.0 82 0.0 83 0.0 84 0.0 85 0.0 86 0.0 87 0.0 88 0.0 89 0.0 90 0.0 91 0.0 92 0.0 93 0.0 94 0.0 95 1.0 96 0.0 97 0.0 98 0.0 99 0.0 100 0.0 101 0.0 102 0.0 103 0.0 104 0.0 105 0.0 106 0.0 107 0.0 108 0.0 109 0.0 110 0.0 111 0.0 112 0.0 113 0.0 114 0.0 115 0.0 116 0.0 117 0.0 118 0.0 119 0.0 120 0.0 121 0.0 122 0.0 123 0.0 124 0.0 125 0.0 126 0.0 127 0.0 128 0.0 129 0.0 130 0.0 131 0.0 132 0.0 133 0.0 134 0.0 135 0.0 136 0.0 137 0.0 138 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0.0 264 0.0 265 0.0 266 0.0 267 0.0 268 0.0 269 0.0 270 0.0 271 0.0 272 0.0 273 0.0 274 0.0 275 0.0 276 0.0 277 0.0 278 0.0 279 0.0 280 0.0 281 0.0 282 0.0 283 0.0 284 0.0 285 0.0 286 0.0 287 0.0 288 0.0 289 0.0 290 0.0 291 0.0 292 0.0 293 0.0 294 0.0 295 0.0 296 0.0 297 0.0 298 0.0 299 0.0 300 0.0 301 0.0 302 0.0 303 0.0 304 0.0 305 0.0 306 0.0 307 0.0 308 0.0 309 0.0 310 0.0 311 0.0 312 0.0 313 0.0 314 0.0 315 0.0 316 0.0 317 0.0 318 0.0 319 0.0 320 0.0 321 0.0 322 0.0 323 0.0 324 0.0 325 0.0 326 1.0 327 0.0 328 0.0 329 0.0 330 0.0 331 0.0 332 0.0 333 0.0 334 0.0 335 0.0 336 0.0 337 0.0 338 0.0 339 0.0 340 0.0 341 0.0 342 0.0 343 0.0 344 0.0 345 0.0 346 0.0 347 0.0 348 0.0 349 1.0 350 0.0 351 0.0 352 0.0 353 0.0 354 0.0 355 0.0 356 0.0 357 0.0 358 0.0 359 0.0 360 0.0 361 0.0 362 0.0 363 0.0 364 0.0 365 0.0 366 0.0 367 0.0 368 0.0 369 0.0 370 0.0 371 0.0 372 0.0 373 0.0 374 0.0 375 0.0 376 0.0 377 0.0 378 0.0 379 0.0 380 0.0 381 0.0 382 0.0 383 0.0 384 0.0 385 0.0 386 0.0 387 0.0 388 0.0 389 0.0 390 0.0 391 0.0 392 0.0 393 0.0 394 0.0 395 0.0 396 0.0 397 0.0 398 0.0 399 0.0 400 0.0 401 0.0 402 0.0 403 0.0 404 1.0 405 0.0 406 0.0 407 0.0 408 0.0 409 0.0 410 0.0 411 0.0 412 0.0 413 0.0 414 0.0 415 0.0 416 0.0 417 0.0 418 0.0 419 0.0 420 0.0 421 0.0 422 0.0 423 0.0 424 0.0 425 0.0 426 0.0 427 0.0 428 0.0 429 0.0 430 0.0 431 0.0 432 0.0 433 0.0 434 0.0 435 0.0 436 1.0 437 0.0 438 0.0 439 0.0 440 0.0 441 0.0 442 0.0 443 0.0 444 0.0 445 0.0 446 0.0 447 0.0 448 0.0 449 0.0 450 0.0 451 0.0 452 0.0 453 0.0 454 0.0 455 0.0 456 0.0 457 0.0 458 0.0 459 0.0 460 0.0 461 0.0 462 0.0 463 0.0 464 0.0 465 0.0 466 0.0 467 0.0 468 0.0 469 0.0 470 0.0 471 0.0 472 0.0 473 0.0 474 0.0 475 0.0 476 1.0 477 0.0 478 0.0 479 0.0 480 0.0 481 0.0 482 0.0 483 0.0 484 0.0 485 0.0 486 0.0 487 0.0 488 0.0 489 0.0 490 0.0 491 0.0 492 0.0 493 0.0 494 0.0 495 0.0 496 0.0 497 0.0 498 1.0 499 0.0 500 0.0 501 0.0 502 0.0 503 0.0 504 0.0 505 0.0 506 0.0 507 0.0 508 0.0 509 0.0 510 0.0 511 0.0 512 0.0 513 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1.0 764 1.0 765 1.0 766 1.0 767 1.0 768 1.0 769 1.0 770 1.0 771 1.0 772 1.0 773 1.0 774 1.0 775 1.0 776 1.0 777 1.0 778 1.0 779 1.0 780 1.0 781 1.0 782 1.0 783 1.0 784 1.0 785 1.0 786 1.0 787 1.0 788 1.0 789 1.0 790 1.0 791 1.0 792 1.0 793 1.0 794 1.0 795 1.0 796 1.0 797 1.0 798 1.0 799 1.0 800 1.0 801 1.0 802 1.0 803 1.0 804 1.0 805 1.0 806 1.0 807 1.0 808 1.0 809 1.0 810 1.0 811 1.0 812 1.0 813 1.0 814 1.0 815 1.0 816 1.0 817 1.0 818 1.0 819 1.0 820 1.0 821 1.0 822 1.0 823 1.0 824 1.0 825 1.0 826 1.0 827 1.0 828 1.0 829 1.0 830 1.0 831 1.0 832 1.0 833 1.0 834 1.0 835 1.0 836 1.0 837 1.0 838 1.0 839 1.0 840 1.0 841 1.0 842 1.0 843 1.0 844 1.0 845 1.0 846 1.0 847 1.0 848 1.0 849 1.0 850 1.0 851 1.0 852 1.0 853 1.0 854 1.0 855 1.0 856 1.0 857 1.0 858 1.0 859 1.0 860 1.0 861 1.0 862 1.0 863 1.0 864 1.0 865 1.0 866 1.0 867 1.0 868 1.0 869 1.0 870 1.0 871 1.0 872 1.0 873 1.0 874 1.0 875 1.0 876 1.0 877 1.0 878 1.0 879 1.0 880 1.0 881 1.0 882 1.0 883 1.0 884 1.0 885 1.0 886 1.0 887 1.0 888 1.0 889 1.0 890 1.0 891 1.0 892 1.0 893 1.0 894 1.0 895 1.0 896 1.0 897 1.0 898 1.0 899 1.0 900 1.0 901 1.0 902 1.0 903 1.0 904 1.0 905 1.0 906 1.0 907 1.0 908 1.0 909 1.0 910 1.0 911 1.0 912 1.0 913 1.0 914 1.0 915 1.0 916 1.0 917 1.0 918 1.0 919 1.0 920 1.0 921 1.0 922 1.0 923 1.0 924 1.0 925 1.0 926 1.0 927 1.0 928 1.0 929 1.0 930 1.0 931 1.0 932 1.0 933 1.0 934 1.0 935 1.0 936 1.0 937 1.0 938 1.0 939 1.0 940 1.0 941 1.0 942 1.0 943 1.0 944 1.0 945 1.0 946 1.0 947 1.0 948 1.0 949 1.0 950 1.0 951 1.0 952 1.0 953 1.0 954 1.0 955 1.0 956 1.0 957 1.0 958 1.0 959 1.0 960 1.0 961 1.0 962 1.0 963 1.0 964 1.0 965 1.0 966 1.0 967 1.0 968 1.0 969 1.0 970 1.0 971 1.0 972 1.0 973 1.0 974 1.0 975 1.0 976 1.0 977 1.0 978 1.0 979 1.0 980 1.0 981 1.0 982 1.0 983 1.0 984 1.0 985 1.0 986 1.0 987 1.0 988 1.0 989 1.0 990 1.0 991 1.0 992 1.0 993 1.0 994 1.0 995 1.0 996 1.0 997 1.0 998 1.0 999 1.0 1000 1.0 1001 1.0 1002 1.0 1003 1.0 1004 1.0 1005 1.0 1006 1.0 1007 1.0 1008 1.0 1009 1.0 1010 1.0 1011 1.0 1012 1.0 1013 1.0 1014 1.0 1015 1.0 1016 1.0 1017 1.0 1018 1.0 1019 1.0 1020 1.0 1021 1.0 1022 1.0 1023 1.0 1024 1.0 1025 1.0 1026 1.0 1027 1.0 1028 1.0 1029 1.0 1030 1.0 1031 1.0 1032 1.0 1033 1.0 1034 1.0 1035 1.0 1036 1.0 1037 1.0 1038 1.0 1039 1.0 1040 1.0 1041 1.0 1042 1.0 1043 1.0 1044 1.0 1045 1.0 1046 1.0 1047 1.0 1048 1.0 1049 1.0 1050 1.0 1051 1.0 1052 1.0 1053 1.0 1054 1.0 1055 1.0 1056 1.0 1057 1.0 1058 1.0 1059 1.0 1060 1.0 1061 1.0 1062 0.0 1063 1.0 1064 1.0 1065 1.0 1066 1.0 1067 1.0 1068 1.0 1069 1.0 1070 1.0 1071 1.0 1072 1.0 1073 1.0 1074 1.0 1075 1.0 1076 1.0 1077 1.0 1078 1.0 1079 1.0 1080 1.0 1081 1.0 1082 1.0 1083 1.0 1084 1.0 1085 1.0 1086 1.0 1087 1.0 1088 1.0 1089 1.0 1090 1.0 1091 1.0 1092 1.0 1093 1.0 1094 1.0 1095 1.0 1096 1.0 1097 1.0 1098 1.0 1099 1.0 1100 1.0 1101 1.0 1102 1.0 1103 1.0 1104 1.0 1105 1.0 1106 1.0 1107 1.0 1108 1.0 1109 1.0 1110 1.0 1111 1.0 1112 1.0 1113 1.0 1114 1.0 1115 1.0 1116 1.0 1117 1.0 1118 1.0 1119 1.0 1120 1.0 1121 1.0 1122 1.0 1123 1.0 1124 1.0 1125 1.0 1126 1.0 1127 1.0 1128 1.0 1129 1.0 1130 1.0 1131 1.0 1132 1.0 1133 1.0 1134 1.0 1135 1.0 1136 1.0 1137 1.0 1138 1.0 1139 1.0 1140 1.0 1141 1.0 1142 1.0 1143 1.0 1144 1.0 1145 1.0 1146 1.0 1147 1.0 1148 1.0 1149 1.0 1150 1.0 1151 1.0 1152 1.0 1153 1.0 1154 1.0 1155 1.0 1156 1.0 1157 1.0 1158 1.0 1159 1.0 1160 1.0 1161 1.0 1162 1.0 1163 1.0 1164 1.0 1165 1.0 1166 1.0 1167 1.0 1168 1.0 1169 1.0 1170 1.0 1171 1.0 1172 1.0 1173 1.0 1174 1.0 1175 1.0 1176 1.0 1177 1.0 1178 1.0 1179 1.0 1180 1.0 1181 1.0 1182 1.0 1183 1.0 1184 1.0 1185 1.0 1186 1.0 1187 1.0 1188 1.0 1189 1.0 1190 1.0 1191 1.0 1192 1.0 1193 1.0 1194 1.0 1195 1.0 1196 1.0 1197 1.0 1198 1.0 1199 1.0 1200 1.0 1201 1.0 1202 1.0 1203 1.0 1204 1.0 1205 1.0 1206 1.0 1207 1.0 1208 1.0 1209 1.0 1210 1.0 1211 1.0 1212 1.0 1213 1.0 1214 1.0 1215 1.0 1216 1.0 1217 1.0 1218 1.0 1219 1.0 1220 1.0 1221 1.0 1222 1.0 1223 1.0 1224 1.0 1225 1.0 1226 1.0 1227 1.0 1228 1.0 1229 1.0 1230 1.0 1231 1.0 1232 1.0 1233 1.0 1234 1.0 1235 1.0 1236 1.0 1237 1.0 1238 1.0 1239 1.0 1240 1.0 1241 1.0 1242 1.0 1243 1.0 1244 1.0 1245 1.0 1246 1.0 1247 1.0 1248 1.0 1249 1.0 1250 1.0 1251 1.0 1252 1.0 1253 1.0 1254 1.0 1255 1.0 1256 1.0 1257 1.0 1258 1.0 1259 1.0 1260 1.0 1261 1.0 1262 1.0 1263 1.0 1264 1.0 1265 1.0 1266 1.0 1267 1.0 1268 1.0 1269 1.0 1270 1.0 1271 1.0 1272 1.0 1273 1.0 1274 1.0 1275 1.0 1276 1.0 1277 1.0 1278 1.0 1279 1.0 1280 1.0 1281 1.0 1282 1.0 1283 1.0 1284 1.0 1285 1.0 1286 1.0 1287 1.0 1288 1.0 1289 1.0 1290 1.0 1291 1.0 1292 1.0 1293 1.0 1294 1.0 1295 1.0 1296 1.0 1297 1.0 1298 1.0 1299 1.0 1300 1.0 1301 1.0 1302 1.0 1303 1.0 1304 1.0 1305 1.0 1306 1.0 1307 1.0 1308 1.0 1309 1.0 1310 1.0 1311 1.0 1312 1.0 1313 1.0 1314 1.0 1315 1.0 1316 1.0 1317 1.0 1318 1.0 1319 1.0 1320 1.0 1321 1.0 1322 1.0 1323 1.0 1324 1.0 1325 1.0 1326 1.0 1327 1.0 1328 1.0 1329 1.0 1330 1.0 1331 1.0 1332 1.0 1333 1.0 1334 1.0 1335 1.0 1336 1.0 1337 1.0 1338 1.0 1339 1.0 1340 1.0 1341 1.0 1342 1.0 1343 1.0 1344 1.0 1345 1.0 1346 1.0 1347 1.0 1348 1.0 1349 1.0 1350 1.0 1351 1.0 1352 1.0 1353 1.0 1354 1.0 1355 1.0 1356 1.0 1357 1.0 1358 1.0 1359 1.0 1360 1.0 1361 1.0 1362 1.0 1363 1.0 1364 1.0 1365 1.0 1366 1.0 1367 1.0 1368 1.0 1369 1.0 1370 1.0 1371 1.0
In [301]:
print("Adj. Rand Index Score: %f." % adjusted_rand_score(y_hat, y))
print("Adj. Mutual Info Score: %f." % adjusted_mutual_info_score(y_hat, y))
print('Classes: ', np.unique(y_hat), 'n_clusters: ', np.unique(y_hat)[-1]+1)
Adj. Rand Index Score: 0.951012. Adj. Mutual Info Score: 0.909999. Classes: [0. 1.] n_clusters: 2.0
RNN Training Part¶
In this section we present batch way to produce the experiment results:
In [324]:
fileList2Train = ["data_sets/00_bunch/banknote/banknote.csv",
"data_sets/00_bunch/glass/glass.csv",
"data_sets/00_bunch/iris/iris.csv",
"data_sets/00_bunch/mnist/mnist.csv",
"data_sets/00_bunch/seeds/seeds.csv",
"data_sets/01_bunch/phoneme/phoneme.csv",
"data_sets/02_bunch/page-blocks/page-blocks.csv"]
datasets = ['banknote', 'glass', 'iris', 'mnist', 'seeds', 'phoneme', 'page-blocks']
# NN Settings
nh = 32
bs = 64
bptt = 20
lr = 1e-2
n_iter = 50
perf_output = []
class Model_Density(nn.Module):
def __init__(self, nv, nh, bs):
super().__init__()
self.nv = nv
self.nh = nh
self.bs = bs
self.i_h = nn.Embedding(self.nv,self.nh)
self.rnn = nn.GRU(self.nh, self.nh, 2, batch_first=True)
self.h_o = nn.Linear(self.nh, self.nv)
self.bn = BatchNorm1dFlat(self.nh)
self.reset()
def reset(self):
self.h = torch.zeros(2, self.bs, self.nh).cuda()
def forward(self, x):
res,h = self.rnn(self.i_h(x), self.h)
self.h = h.detach()
return self.h_o(self.bn(res))
for ifn, fn2T in enumerate(fileList2Train):
print('\n\nFile Name to Train: ', fn2T)
print('#############################################################')
data0 = pd.read_csv(fn2T, header=None)
X = data0.iloc[:,:-1].values
y = data0.iloc[:, -1].values
if X.shape[1] > 4:
X = umap.UMAP(
n_neighbors=50,
min_dist=0.0,
n_components=4,
random_state=seed,
).fit_transform(X)
#parameter definition
best_min_cluster_size = 17
if X.shape[0] > 1000:
best_min_cluster_size = 125
best_min_sample_size = 3
best_min_mode_size = best_min_cluster_size
# HDBSCAN
print('HDBSCAN')
print('#############################################################')
clusterer = hdbscan.HDBSCAN(min_cluster_size=best_min_cluster_size, min_samples=best_min_sample_size)
y_hat = clusterer.fit_predict(X)
print("Adj. Rand Index Score: %f." % adjusted_rand_score(y_hat, y))
print("Adj. Mutual Info Score: %f." % adjusted_mutual_info_score(y_hat, y))
print('Classes: ', np.unique(y_hat), 'n_clusters: ', np.unique(y_hat)[-1]+1)
perf_output.append((adjusted_rand_score(y_hat, y), adjusted_mutual_info_score(y_hat, y)))
# QSHIFTPP
print('QSHIFTPP')
print('#############################################################')
X_qspp = X.copy(order='C')
k=25
if X.shape [0] > 1000:
k = 60
model = QuickshiftPP(k=k, beta=.6)
# Compute the clustering.
model.fit(X_qspp)
y_hat = model.memberships
print("Adj. Rand Index Score: %f." % adjusted_rand_score(y_hat, y))
print("Adj. Mutual Info Score: %f." % adjusted_mutual_info_score(y_hat, y))
print('Classes: ', np.unique(y_hat), 'n_clusters: ', np.unique(y_hat)[-1]+1)
perf_output.append((adjusted_rand_score(y_hat, y), adjusted_mutual_info_score(y_hat, y)))
#generate paths for RNN with HQSHIFT
print('HQSHIFT')
print('#############################################################')
hqshift = HQSHIFT(min_cluster_size=best_min_cluster_size,
min_mode_size=best_min_mode_size,
min_samples=best_min_sample_size,
allow_single_cluster=False).fit(X)
# cluster labels
y_hat = hqshift.labels_
print("Adj. Rand Index Score: %f." % adjusted_rand_score(y_hat, y))
print("Adj. Mutual Info Score: %f." % adjusted_mutual_info_score(y_hat, y))
print('Classes: ', np.unique(y_hat), 'n_clusters: ', np.unique(y_hat)[-1]+1)
clust_paths = [[j]+pth[:pth.index(idx)+1] for idx in hqshift._idx_root
for j, pth in enumerate(hqshift.paths) if idx in pth]
clust_data = [X[pth] for pth in clust_paths]
# Construct Vocabulary, make sure BOS (<xxbos>) idx is 0
vocab_tok = [BOS] + [str(idx) for idx in np.arange(X.shape[0])]
# => ['<pad>', '0', '1', '2', '3',... 'seq_len-1']
vocab_ = {idx:tok for idx,tok in enumerate(vocab_tok)}
ids = [[0]+list(np.array(pth)+1) for pth in clust_paths]
vocab=Vocab(vocab_)
nv = len(vocab.itos)
ids = np.array(ids)
data = TextLMDataBunch.from_ids('/tmp', vocab=vocab,
train_ids=ids, valid_ids=ids,
bs=bs, bptt=bptt, device=device)
for inet in range(0, 10):
#Learner
random_seed(seed_value=seed_lst[inet], use_cuda=use_cuda)
print('Seed: ', seed_lst[inet])
learn = Learner(data, Model_Density(nv, nh, bs), metrics=accuracy)
learn.fit_one_cycle(n_iter, lr);
learn.recorder.plot_losses()
plt.show()
learn.recorder.plot_metrics()
plt.xlabel('Batches Processed')
plt.ylabel('Accuracy [%]')
plt.show()
learn.save(datapath4file(str(URLs.LOCAL_PATH/'models')+'/'+datasets[ifn]+'%02d'%inet), return_path=True)
print('Paths: ', len(learn.data.train_ds), ', Tokens: ', X.shape[0])
stop_cond = sorted([ridx+1 for ridx in hqshift._idx_root])
print(stop_cond)
# calculate cluster validation scores with trained RNN
y_hat = -2*np.ones(X.shape[0])
for q_p in range(X.shape[0]):
m = learn.model
#set batch size to 1
learn.model.bs=1
#turn off dropout
m.eval()
#reset hidden state
m.reset()
idxs = np.array([0, q_p+1])
idxs = torch.tensor(idxs, device=device)
# import pdb; pdb.set_trace()
for _ in range(bptt):
p = m.forward(idxs[None,:])
cls = F.softmax(p[0], dim=1).argmax(dim=1)
if cls[-1].item()==0: print(0); break
idxs = torch.cat((idxs, cls[-1].unsqueeze(0)))
if cls[-1].item() in stop_cond: break
# import pdb;pdb.set_trace()
#set the class label from the last index of the trajectory produced by HQuickShift
y_hat[q_p] = hqshift.labels_[idxs[-1]-1]
# print(q_p, y_hat[q_p])
print("Adj. Rand Index Score: %f." % adjusted_rand_score(y_hat, y))
print("Adj. Mutual Info Score: %f." % adjusted_mutual_info_score(y_hat, y))
perf_output.append((adjusted_rand_score(y_hat, y), adjusted_mutual_info_score(y_hat, y)))
print('Classes: ', np.unique(y_hat), 'n_clusters: ', np.unique(y_hat)[-1]+1)
File Name to Train: data_sets/00_bunch/banknote/banknote.csv ############################################################# HDBSCAN ############################################################# Adj. Rand Index Score: 0.792122. Adj. Mutual Info Score: 0.625601. Classes: [-1 0 1] n_clusters: 2 QSHIFTPP ############################################################# Adj. Rand Index Score: 0.690146. Adj. Mutual Info Score: 0.555389. Classes: [0 1 2 3] n_clusters: 4 HQSHIFT ############################################################# Adj. Rand Index Score: 0.798521. Adj. Mutual Info Score: 0.632384. Classes: [-1 0 1] n_clusters: 2 Seed: 0
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 7.044263 | 6.920993 | 0.034001 | 00:00 |
| 2 | 6.709130 | 6.297531 | 0.356641 | 00:00 |
| 3 | 6.107814 | 5.237638 | 0.546450 | 00:00 |
| 4 | 4.962787 | 3.358989 | 0.641525 | 00:00 |
| 5 | 3.415560 | 1.836544 | 0.730503 | 00:00 |
| 6 | 2.213147 | 1.153419 | 0.809918 | 00:00 |
| 7 | 1.438570 | 0.770817 | 0.873302 | 00:00 |
| 8 | 0.972705 | 0.585025 | 0.898947 | 00:00 |
| 9 | 0.715531 | 0.502509 | 0.910785 | 00:00 |
| 10 | 0.578538 | 0.474654 | 0.915591 | 00:00 |
| 11 | 0.508781 | 0.454988 | 0.918088 | 00:00 |
| 12 | 0.474802 | 0.448446 | 0.918699 | 00:00 |
| 13 | 0.456393 | 0.439007 | 0.918733 | 00:00 |
| 14 | 0.447378 | 0.439361 | 0.918716 | 00:00 |
| 15 | 0.444240 | 0.437068 | 0.918648 | 00:00 |
| 16 | 0.440355 | 0.436687 | 0.916457 | 00:00 |
| 17 | 0.439709 | 0.438742 | 0.918784 | 00:00 |
| 18 | 0.437227 | 0.432368 | 0.918733 | 00:00 |
| 19 | 0.437289 | 0.433192 | 0.918733 | 00:00 |
| 20 | 0.435506 | 0.438019 | 0.914368 | 00:00 |
| 21 | 0.434909 | 0.428678 | 0.916899 | 00:00 |
| 22 | 0.432849 | 0.431241 | 0.918716 | 00:00 |
| 23 | 0.433434 | 0.432313 | 0.918580 | 00:00 |
| 24 | 0.433142 | 0.427785 | 0.918716 | 00:00 |
| 25 | 0.433027 | 0.428560 | 0.918716 | 00:00 |
| 26 | 0.430004 | 0.428991 | 0.918835 | 00:00 |
| 27 | 0.431269 | 0.428593 | 0.918529 | 00:00 |
| 28 | 0.430494 | 0.429136 | 0.918716 | 00:00 |
| 29 | 0.429403 | 0.428048 | 0.918767 | 00:00 |
| 30 | 0.428515 | 0.427745 | 0.918767 | 00:00 |
| 31 | 0.427537 | 0.426975 | 0.918733 | 00:00 |
| 32 | 0.427245 | 0.426009 | 0.918733 | 00:00 |
| 33 | 0.427191 | 0.424321 | 0.918716 | 00:00 |
| 34 | 0.426557 | 0.424623 | 0.918733 | 00:00 |
| 35 | 0.425969 | 0.425220 | 0.918546 | 00:00 |
| 36 | 0.426189 | 0.423173 | 0.918869 | 00:00 |
| 37 | 0.425272 | 0.423195 | 0.918818 | 00:00 |
| 38 | 0.424256 | 0.422451 | 0.918767 | 00:00 |
| 39 | 0.423347 | 0.421978 | 0.918784 | 00:00 |
| 40 | 0.423587 | 0.422239 | 0.918818 | 00:00 |
| 41 | 0.423398 | 0.421616 | 0.918733 | 00:00 |
| 42 | 0.423496 | 0.421372 | 0.918801 | 00:00 |
| 43 | 0.423867 | 0.421241 | 0.918835 | 00:00 |
| 44 | 0.422994 | 0.421092 | 0.918852 | 00:00 |
| 45 | 0.422552 | 0.420766 | 0.918818 | 00:00 |
| 46 | 0.421451 | 0.420684 | 0.918818 | 00:00 |
| 47 | 0.420899 | 0.420724 | 0.918767 | 00:00 |
| 48 | 0.421617 | 0.420717 | 0.918852 | 00:00 |
| 49 | 0.421566 | 0.420641 | 0.918784 | 00:00 |
| 50 | 0.421076 | 0.420732 | 0.918750 | 00:00 |
/home/can/anaconda3/envs/hqshift/lib/python3.6/site-packages/fastai/datasets.py:153: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details. with open(fpath, 'r') as yaml_file: return yaml.load(yaml_file)
Paths: 3058 , Tokens: 1372 [36, 64, 181, 1003, 1063, 1335] Adj. Rand Index Score: 0.951012. Adj. Mutual Info Score: 0.909999. Classes: [0. 1.] n_clusters: 2.0 Seed: 23
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 7.053731 | 6.923075 | 0.075798 | 00:00 |
| 2 | 6.705861 | 6.291873 | 0.308509 | 00:00 |
| 3 | 6.091847 | 5.203226 | 0.537415 | 00:00 |
| 4 | 4.940405 | 3.343619 | 0.632269 | 00:00 |
| 5 | 3.405858 | 1.835058 | 0.734885 | 00:00 |
| 6 | 2.198524 | 1.134763 | 0.816746 | 00:00 |
| 7 | 1.427225 | 0.770289 | 0.864419 | 00:00 |
| 8 | 0.969620 | 0.586738 | 0.900136 | 00:00 |
| 9 | 0.715642 | 0.499854 | 0.912245 | 00:00 |
| 10 | 0.579251 | 0.464015 | 0.916814 | 00:00 |
| 11 | 0.507217 | 0.455231 | 0.916372 | 00:00 |
| 12 | 0.473177 | 0.455472 | 0.918665 | 00:00 |
| 13 | 0.457231 | 0.446979 | 0.918750 | 00:00 |
| 14 | 0.449020 | 0.443722 | 0.918631 | 00:00 |
| 15 | 0.443532 | 0.438399 | 0.918750 | 00:00 |
| 16 | 0.440627 | 0.434290 | 0.918665 | 00:00 |
| 17 | 0.438735 | 0.438620 | 0.918682 | 00:00 |
| 18 | 0.437438 | 0.434133 | 0.918648 | 00:00 |
| 19 | 0.436779 | 0.434982 | 0.914759 | 00:00 |
| 20 | 0.435304 | 0.438316 | 0.918716 | 00:00 |
| 21 | 0.433747 | 0.438539 | 0.912908 | 00:00 |
| 22 | 0.433081 | 0.436179 | 0.918750 | 00:00 |
| 23 | 0.430437 | 0.432759 | 0.918801 | 00:00 |
| 24 | 0.429796 | 0.429544 | 0.918733 | 00:00 |
| 25 | 0.429453 | 0.430152 | 0.918852 | 00:00 |
| 26 | 0.428877 | 0.426628 | 0.918733 | 00:00 |
| 27 | 0.428120 | 0.427818 | 0.918665 | 00:00 |
| 28 | 0.428686 | 0.428132 | 0.918767 | 00:00 |
| 29 | 0.427650 | 0.425387 | 0.918784 | 00:00 |
| 30 | 0.427140 | 0.425413 | 0.918852 | 00:00 |
| 31 | 0.427017 | 0.427611 | 0.918767 | 00:00 |
| 32 | 0.425495 | 0.423974 | 0.918801 | 00:00 |
| 33 | 0.424709 | 0.423743 | 0.918784 | 00:00 |
| 34 | 0.425142 | 0.424183 | 0.918801 | 00:00 |
| 35 | 0.422795 | 0.423450 | 0.918818 | 00:00 |
| 36 | 0.423353 | 0.423185 | 0.918818 | 00:00 |
| 37 | 0.424462 | 0.422455 | 0.918801 | 00:00 |
| 38 | 0.423538 | 0.422485 | 0.918818 | 00:00 |
| 39 | 0.422874 | 0.421994 | 0.918818 | 00:00 |
| 40 | 0.423245 | 0.421959 | 0.918835 | 00:00 |
| 41 | 0.423398 | 0.421555 | 0.918852 | 00:00 |
| 42 | 0.422301 | 0.421106 | 0.918801 | 00:00 |
| 43 | 0.423346 | 0.420832 | 0.918818 | 00:00 |
| 44 | 0.422480 | 0.420718 | 0.918801 | 00:00 |
| 45 | 0.423267 | 0.420693 | 0.918835 | 00:00 |
| 46 | 0.422162 | 0.420550 | 0.918818 | 00:00 |
| 47 | 0.421325 | 0.420569 | 0.918835 | 00:00 |
| 48 | 0.421699 | 0.420551 | 0.918801 | 00:00 |
| 49 | 0.419644 | 0.420537 | 0.918801 | 00:00 |
| 50 | 0.420277 | 0.420509 | 0.918801 | 00:00 |
Paths: 3058 , Tokens: 1372 [36, 64, 181, 1003, 1063, 1335] Adj. Rand Index Score: 0.951012. Adj. Mutual Info Score: 0.909999. Classes: [0. 1.] n_clusters: 2.0 Seed: 42
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 6.987081 | 6.826691 | 0.104535 | 00:00 |
| 2 | 6.564578 | 6.066473 | 0.326613 | 00:00 |
| 3 | 5.871880 | 4.897140 | 0.512704 | 00:00 |
| 4 | 4.678733 | 3.105808 | 0.634222 | 00:00 |
| 5 | 3.199978 | 1.725034 | 0.743835 | 00:00 |
| 6 | 2.071503 | 1.091280 | 0.821246 | 00:00 |
| 7 | 1.365065 | 0.754912 | 0.873013 | 00:00 |
| 8 | 0.941880 | 0.592287 | 0.898115 | 00:00 |
| 9 | 0.702985 | 0.504306 | 0.909273 | 00:00 |
| 10 | 0.574382 | 0.468194 | 0.916661 | 00:00 |
| 11 | 0.505428 | 0.449421 | 0.918224 | 00:00 |
| 12 | 0.471008 | 0.453906 | 0.916508 | 00:00 |
| 13 | 0.457015 | 0.453297 | 0.915829 | 00:00 |
| 14 | 0.451132 | 0.451250 | 0.918682 | 00:00 |
| 15 | 0.444567 | 0.439409 | 0.918325 | 00:00 |
| 16 | 0.443640 | 0.438300 | 0.913689 | 00:00 |
| 17 | 0.441540 | 0.440561 | 0.918393 | 00:00 |
| 18 | 0.437770 | 0.436433 | 0.918699 | 00:00 |
| 19 | 0.436996 | 0.433346 | 0.918784 | 00:00 |
| 20 | 0.435860 | 0.430529 | 0.918784 | 00:00 |
| 21 | 0.435087 | 0.431886 | 0.918665 | 00:00 |
| 22 | 0.433687 | 0.430996 | 0.918648 | 00:00 |
| 23 | 0.433275 | 0.431775 | 0.918597 | 00:00 |
| 24 | 0.432019 | 0.434081 | 0.918614 | 00:00 |
| 25 | 0.431902 | 0.430050 | 0.918801 | 00:00 |
| 26 | 0.431993 | 0.427122 | 0.918784 | 00:00 |
| 27 | 0.431229 | 0.429217 | 0.918733 | 00:00 |
| 28 | 0.430194 | 0.425386 | 0.918784 | 00:00 |
| 29 | 0.430812 | 0.427755 | 0.918869 | 00:00 |
| 30 | 0.429447 | 0.425156 | 0.916593 | 00:00 |
| 31 | 0.427334 | 0.426781 | 0.918733 | 00:00 |
| 32 | 0.427351 | 0.426650 | 0.918852 | 00:00 |
| 33 | 0.427379 | 0.425408 | 0.918716 | 00:00 |
| 34 | 0.427864 | 0.426067 | 0.918767 | 00:00 |
| 35 | 0.425298 | 0.423778 | 0.918835 | 00:00 |
| 36 | 0.424343 | 0.424733 | 0.918750 | 00:00 |
| 37 | 0.425830 | 0.423472 | 0.918801 | 00:00 |
| 38 | 0.423263 | 0.422754 | 0.918733 | 00:00 |
| 39 | 0.423025 | 0.422460 | 0.918767 | 00:00 |
| 40 | 0.423453 | 0.422227 | 0.918716 | 00:00 |
| 41 | 0.423430 | 0.421678 | 0.918784 | 00:00 |
| 42 | 0.423687 | 0.421368 | 0.918835 | 00:00 |
| 43 | 0.422789 | 0.420994 | 0.918818 | 00:00 |
| 44 | 0.420430 | 0.421228 | 0.918818 | 00:00 |
| 45 | 0.421983 | 0.421247 | 0.918767 | 00:00 |
| 46 | 0.422362 | 0.420924 | 0.918852 | 00:00 |
| 47 | 0.421204 | 0.420894 | 0.918784 | 00:00 |
| 48 | 0.420436 | 0.420927 | 0.918801 | 00:00 |
| 49 | 0.422097 | 0.420892 | 0.918750 | 00:00 |
| 50 | 0.421451 | 0.420992 | 0.918767 | 00:00 |
Paths: 3058 , Tokens: 1372 [36, 64, 181, 1003, 1063, 1335] Adj. Rand Index Score: 0.951012. Adj. Mutual Info Score: 0.909999. Classes: [0. 1.] n_clusters: 2.0 Seed: 1234
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 7.041072 | 6.925368 | 0.062143 | 00:00 |
| 2 | 6.719156 | 6.328650 | 0.317086 | 00:00 |
| 3 | 6.139575 | 5.294110 | 0.494480 | 00:00 |
| 4 | 5.017441 | 3.409136 | 0.610955 | 00:00 |
| 5 | 3.482327 | 1.907480 | 0.719005 | 00:00 |
| 6 | 2.258461 | 1.171008 | 0.813638 | 00:00 |
| 7 | 1.461064 | 0.773753 | 0.867086 | 00:00 |
| 8 | 0.979859 | 0.592329 | 0.898251 | 00:00 |
| 9 | 0.719229 | 0.501040 | 0.907677 | 00:00 |
| 10 | 0.581269 | 0.479114 | 0.916117 | 00:00 |
| 11 | 0.510892 | 0.455712 | 0.917867 | 00:00 |
| 12 | 0.474027 | 0.450686 | 0.918461 | 00:00 |
| 13 | 0.457504 | 0.449055 | 0.918767 | 00:00 |
| 14 | 0.447438 | 0.439815 | 0.918682 | 00:00 |
| 15 | 0.442772 | 0.433869 | 0.918699 | 00:00 |
| 16 | 0.441110 | 0.436493 | 0.916559 | 00:00 |
| 17 | 0.439615 | 0.435613 | 0.918224 | 00:00 |
| 18 | 0.437315 | 0.436451 | 0.916474 | 00:00 |
| 19 | 0.435990 | 0.430930 | 0.918818 | 00:00 |
| 20 | 0.432648 | 0.435101 | 0.918767 | 00:00 |
| 21 | 0.431487 | 0.431179 | 0.916627 | 00:00 |
| 22 | 0.430845 | 0.429775 | 0.918699 | 00:00 |
| 23 | 0.431270 | 0.432654 | 0.918835 | 00:00 |
| 24 | 0.430959 | 0.429887 | 0.913689 | 00:00 |
| 25 | 0.430123 | 0.428822 | 0.917629 | 00:00 |
| 26 | 0.430601 | 0.426298 | 0.918784 | 00:00 |
| 27 | 0.429596 | 0.425177 | 0.918784 | 00:00 |
| 28 | 0.428499 | 0.427447 | 0.918784 | 00:00 |
| 29 | 0.428706 | 0.428138 | 0.918801 | 00:00 |
| 30 | 0.427106 | 0.423735 | 0.918784 | 00:00 |
| 31 | 0.427071 | 0.425217 | 0.918784 | 00:00 |
| 32 | 0.425971 | 0.424252 | 0.918767 | 00:00 |
| 33 | 0.425205 | 0.424537 | 0.918835 | 00:00 |
| 34 | 0.424279 | 0.423838 | 0.918784 | 00:00 |
| 35 | 0.424163 | 0.425170 | 0.918801 | 00:00 |
| 36 | 0.423792 | 0.422845 | 0.918767 | 00:00 |
| 37 | 0.422903 | 0.421615 | 0.918767 | 00:00 |
| 38 | 0.422389 | 0.422464 | 0.918818 | 00:00 |
| 39 | 0.422402 | 0.421632 | 0.918801 | 00:00 |
| 40 | 0.423566 | 0.421729 | 0.918869 | 00:00 |
| 41 | 0.423396 | 0.421433 | 0.918801 | 00:00 |
| 42 | 0.421717 | 0.421293 | 0.918784 | 00:00 |
| 43 | 0.421680 | 0.421047 | 0.918767 | 00:00 |
| 44 | 0.421123 | 0.420807 | 0.918750 | 00:00 |
| 45 | 0.420797 | 0.420695 | 0.918801 | 00:00 |
| 46 | 0.420417 | 0.420614 | 0.918801 | 00:00 |
| 47 | 0.420685 | 0.420612 | 0.918818 | 00:00 |
| 48 | 0.419477 | 0.420571 | 0.918801 | 00:00 |
| 49 | 0.419370 | 0.420672 | 0.918716 | 00:00 |
| 50 | 0.419966 | 0.420539 | 0.918784 | 00:00 |
Paths: 3058 , Tokens: 1372 [36, 64, 181, 1003, 1063, 1335] Adj. Rand Index Score: 0.951012. Adj. Mutual Info Score: 0.909999. Classes: [0. 1.] n_clusters: 2.0 Seed: 43210
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 7.000233 | 6.864887 | 0.087007 | 00:00 |
| 2 | 6.638663 | 6.205266 | 0.306301 | 00:00 |
| 3 | 6.007813 | 5.123574 | 0.482490 | 00:00 |
| 4 | 4.837506 | 3.245512 | 0.635564 | 00:00 |
| 5 | 3.319189 | 1.799670 | 0.746501 | 00:00 |
| 6 | 2.148674 | 1.116702 | 0.820007 | 00:00 |
| 7 | 1.398739 | 0.770765 | 0.869039 | 00:00 |
| 8 | 0.953240 | 0.581061 | 0.899660 | 00:00 |
| 9 | 0.705338 | 0.504975 | 0.911872 | 00:00 |
| 10 | 0.574363 | 0.471839 | 0.912755 | 00:00 |
| 11 | 0.507599 | 0.461713 | 0.918190 | 00:00 |
| 12 | 0.473118 | 0.449559 | 0.918461 | 00:00 |
| 13 | 0.458132 | 0.447005 | 0.918682 | 00:00 |
| 14 | 0.450020 | 0.441749 | 0.911872 | 00:00 |
| 15 | 0.445066 | 0.441291 | 0.915625 | 00:00 |
| 16 | 0.440497 | 0.435241 | 0.918648 | 00:00 |
| 17 | 0.440310 | 0.438298 | 0.918699 | 00:00 |
| 18 | 0.438940 | 0.438771 | 0.918682 | 00:00 |
| 19 | 0.437408 | 0.433363 | 0.918750 | 00:00 |
| 20 | 0.437045 | 0.432919 | 0.918699 | 00:00 |
| 21 | 0.435915 | 0.433297 | 0.918801 | 00:00 |
| 22 | 0.433438 | 0.432543 | 0.918716 | 00:00 |
| 23 | 0.431512 | 0.430205 | 0.918767 | 00:00 |
| 24 | 0.431592 | 0.432824 | 0.918665 | 00:00 |
| 25 | 0.432389 | 0.430447 | 0.918784 | 00:00 |
| 26 | 0.431402 | 0.429243 | 0.916593 | 00:00 |
| 27 | 0.430109 | 0.428114 | 0.918818 | 00:00 |
| 28 | 0.430301 | 0.429901 | 0.918852 | 00:00 |
| 29 | 0.429586 | 0.427824 | 0.918852 | 00:00 |
| 30 | 0.427872 | 0.427190 | 0.918682 | 00:00 |
| 31 | 0.427868 | 0.427096 | 0.918784 | 00:00 |
| 32 | 0.427223 | 0.426341 | 0.918852 | 00:00 |
| 33 | 0.426060 | 0.425791 | 0.918801 | 00:00 |
| 34 | 0.426422 | 0.425096 | 0.918767 | 00:00 |
| 35 | 0.425961 | 0.424575 | 0.918750 | 00:00 |
| 36 | 0.424634 | 0.423621 | 0.918835 | 00:00 |
| 37 | 0.424830 | 0.423781 | 0.918869 | 00:00 |
| 38 | 0.424511 | 0.422329 | 0.918767 | 00:00 |
| 39 | 0.423325 | 0.422689 | 0.918767 | 00:00 |
| 40 | 0.423530 | 0.422266 | 0.918852 | 00:00 |
| 41 | 0.423336 | 0.421982 | 0.918682 | 00:00 |
| 42 | 0.420949 | 0.421729 | 0.918784 | 00:00 |
| 43 | 0.423495 | 0.421139 | 0.918920 | 00:00 |
| 44 | 0.423161 | 0.421075 | 0.918801 | 00:00 |
| 45 | 0.421790 | 0.420817 | 0.918920 | 00:00 |
| 46 | 0.421506 | 0.420869 | 0.918818 | 00:00 |
| 47 | 0.420549 | 0.420756 | 0.918818 | 00:00 |
| 48 | 0.420940 | 0.420681 | 0.918767 | 00:00 |
| 49 | 0.421933 | 0.420645 | 0.918801 | 00:00 |
| 50 | 0.420552 | 0.420679 | 0.918801 | 00:00 |
Paths: 3058 , Tokens: 1372 [36, 64, 181, 1003, 1063, 1335] Adj. Rand Index Score: 0.951012. Adj. Mutual Info Score: 0.909999. Classes: [0. 1.] n_clusters: 2.0 Seed: 1133557799
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 7.049446 | 6.949334 | 0.055095 | 00:00 |
| 2 | 6.761670 | 6.400378 | 0.330961 | 00:00 |
| 3 | 6.218757 | 5.409724 | 0.545550 | 00:00 |
| 4 | 5.120669 | 3.535824 | 0.613706 | 00:00 |
| 5 | 3.556817 | 1.955474 | 0.713587 | 00:00 |
| 6 | 2.304996 | 1.193196 | 0.807320 | 00:00 |
| 7 | 1.487150 | 0.780008 | 0.868257 | 00:00 |
| 8 | 0.997112 | 0.584172 | 0.899440 | 00:00 |
| 9 | 0.724906 | 0.499759 | 0.908747 | 00:00 |
| 10 | 0.583030 | 0.465774 | 0.916593 | 00:00 |
| 11 | 0.508723 | 0.453594 | 0.918020 | 00:00 |
| 12 | 0.472751 | 0.446006 | 0.916321 | 00:00 |
| 13 | 0.458666 | 0.442097 | 0.913043 | 00:00 |
| 14 | 0.449044 | 0.440206 | 0.918597 | 00:00 |
| 15 | 0.441792 | 0.441118 | 0.918614 | 00:00 |
| 16 | 0.441676 | 0.439240 | 0.912432 | 00:00 |
| 17 | 0.438625 | 0.436639 | 0.918597 | 00:00 |
| 18 | 0.439759 | 0.435743 | 0.918716 | 00:00 |
| 19 | 0.434879 | 0.429838 | 0.918733 | 00:00 |
| 20 | 0.435320 | 0.431340 | 0.918733 | 00:00 |
| 21 | 0.434061 | 0.431900 | 0.918614 | 00:00 |
| 22 | 0.434051 | 0.432633 | 0.918648 | 00:00 |
| 23 | 0.433497 | 0.426929 | 0.918767 | 00:00 |
| 24 | 0.430877 | 0.429723 | 0.918784 | 00:00 |
| 25 | 0.430271 | 0.432859 | 0.918733 | 00:00 |
| 26 | 0.430525 | 0.430608 | 0.918767 | 00:00 |
| 27 | 0.428503 | 0.430302 | 0.918784 | 00:00 |
| 28 | 0.428112 | 0.426868 | 0.918784 | 00:00 |
| 29 | 0.428677 | 0.426774 | 0.918801 | 00:00 |
| 30 | 0.428151 | 0.426868 | 0.918852 | 00:00 |
| 31 | 0.427076 | 0.426058 | 0.918835 | 00:00 |
| 32 | 0.425716 | 0.425197 | 0.918818 | 00:00 |
| 33 | 0.425206 | 0.423866 | 0.918801 | 00:00 |
| 34 | 0.424298 | 0.423408 | 0.918784 | 00:00 |
| 35 | 0.424790 | 0.423344 | 0.918733 | 00:00 |
| 36 | 0.424036 | 0.423532 | 0.918801 | 00:00 |
| 37 | 0.423947 | 0.422492 | 0.918835 | 00:00 |
| 38 | 0.424023 | 0.422772 | 0.918903 | 00:00 |
| 39 | 0.423149 | 0.422385 | 0.918818 | 00:00 |
| 40 | 0.422681 | 0.421667 | 0.918835 | 00:00 |
| 41 | 0.421699 | 0.421557 | 0.918852 | 00:00 |
| 42 | 0.421929 | 0.421199 | 0.918869 | 00:00 |
| 43 | 0.421374 | 0.421039 | 0.918818 | 00:00 |
| 44 | 0.422078 | 0.420954 | 0.918784 | 00:00 |
| 45 | 0.421715 | 0.420715 | 0.918835 | 00:00 |
| 46 | 0.421536 | 0.420683 | 0.918818 | 00:00 |
| 47 | 0.421369 | 0.420682 | 0.918818 | 00:00 |
| 48 | 0.421097 | 0.420665 | 0.918852 | 00:00 |
| 49 | 0.420322 | 0.420654 | 0.918818 | 00:00 |
| 50 | 0.420508 | 0.420675 | 0.918801 | 00:00 |
Paths: 3058 , Tokens: 1372 [36, 64, 181, 1003, 1063, 1335] Adj. Rand Index Score: 0.951012. Adj. Mutual Info Score: 0.909999. Classes: [0. 1.] n_clusters: 2.0 Seed: 22446688
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 7.035093 | 6.891237 | 0.096128 | 00:00 |
| 2 | 6.659505 | 6.200901 | 0.341695 | 00:00 |
| 3 | 6.018675 | 5.088204 | 0.548234 | 00:00 |
| 4 | 4.832672 | 3.227494 | 0.632082 | 00:00 |
| 5 | 3.310291 | 1.792130 | 0.734884 | 00:00 |
| 6 | 2.147572 | 1.133278 | 0.817086 | 00:00 |
| 7 | 1.401762 | 0.757517 | 0.873217 | 00:00 |
| 8 | 0.957925 | 0.578125 | 0.899439 | 00:00 |
| 9 | 0.709361 | 0.506308 | 0.905010 | 00:00 |
| 10 | 0.576873 | 0.464529 | 0.916423 | 00:00 |
| 11 | 0.509092 | 0.455843 | 0.918003 | 00:00 |
| 12 | 0.474346 | 0.449380 | 0.918377 | 00:00 |
| 13 | 0.458797 | 0.446028 | 0.918580 | 00:00 |
| 14 | 0.449634 | 0.441383 | 0.918716 | 00:00 |
| 15 | 0.443025 | 0.444376 | 0.913808 | 00:00 |
| 16 | 0.440938 | 0.440231 | 0.918716 | 00:00 |
| 17 | 0.439740 | 0.437772 | 0.918716 | 00:00 |
| 18 | 0.439452 | 0.439375 | 0.918563 | 00:00 |
| 19 | 0.436726 | 0.440745 | 0.918699 | 00:00 |
| 20 | 0.435715 | 0.436867 | 0.918665 | 00:00 |
| 21 | 0.435113 | 0.431694 | 0.917561 | 00:00 |
| 22 | 0.434474 | 0.434232 | 0.918767 | 00:00 |
| 23 | 0.432918 | 0.431985 | 0.918733 | 00:00 |
| 24 | 0.432127 | 0.430401 | 0.918699 | 00:00 |
| 25 | 0.432430 | 0.429111 | 0.918699 | 00:00 |
| 26 | 0.431258 | 0.426426 | 0.918750 | 00:00 |
| 27 | 0.430045 | 0.429508 | 0.918835 | 00:00 |
| 28 | 0.429302 | 0.428440 | 0.918784 | 00:00 |
| 29 | 0.427248 | 0.425337 | 0.918784 | 00:00 |
| 30 | 0.426378 | 0.426546 | 0.918733 | 00:00 |
| 31 | 0.426356 | 0.427742 | 0.918733 | 00:00 |
| 32 | 0.426253 | 0.427252 | 0.918682 | 00:00 |
| 33 | 0.426043 | 0.424929 | 0.918750 | 00:00 |
| 34 | 0.426505 | 0.424642 | 0.918716 | 00:00 |
| 35 | 0.424912 | 0.425569 | 0.918784 | 00:00 |
| 36 | 0.425300 | 0.423565 | 0.918716 | 00:00 |
| 37 | 0.424603 | 0.423061 | 0.918784 | 00:00 |
| 38 | 0.423622 | 0.423866 | 0.918733 | 00:00 |
| 39 | 0.423569 | 0.422139 | 0.918818 | 00:00 |
| 40 | 0.421647 | 0.421659 | 0.918801 | 00:00 |
| 41 | 0.422032 | 0.421945 | 0.918767 | 00:00 |
| 42 | 0.423075 | 0.421543 | 0.918801 | 00:00 |
| 43 | 0.421785 | 0.421447 | 0.918733 | 00:00 |
| 44 | 0.422130 | 0.421138 | 0.918818 | 00:00 |
| 45 | 0.421442 | 0.421086 | 0.918767 | 00:00 |
| 46 | 0.421997 | 0.420918 | 0.918801 | 00:00 |
| 47 | 0.422327 | 0.420826 | 0.918818 | 00:00 |
| 48 | 0.421020 | 0.420877 | 0.918784 | 00:00 |
| 49 | 0.420310 | 0.420940 | 0.918750 | 00:00 |
| 50 | 0.421524 | 0.420804 | 0.918784 | 00:00 |
Paths: 3058 , Tokens: 1372 [36, 64, 181, 1003, 1063, 1335] Adj. Rand Index Score: 0.951012. Adj. Mutual Info Score: 0.909999. Classes: [0. 1.] n_clusters: 2.0 Seed: 123456789
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 7.017137 | 6.878190 | 0.087840 | 00:00 |
| 2 | 6.643477 | 6.197146 | 0.320312 | 00:00 |
| 3 | 6.006318 | 5.110162 | 0.521535 | 00:00 |
| 4 | 4.844345 | 3.209101 | 0.638264 | 00:00 |
| 5 | 3.316853 | 1.795212 | 0.736498 | 00:00 |
| 6 | 2.147209 | 1.115885 | 0.818818 | 00:00 |
| 7 | 1.399731 | 0.753382 | 0.870924 | 00:00 |
| 8 | 0.954093 | 0.587307 | 0.898251 | 00:00 |
| 9 | 0.707781 | 0.502579 | 0.911073 | 00:00 |
| 10 | 0.576721 | 0.466429 | 0.911141 | 00:00 |
| 11 | 0.508777 | 0.460322 | 0.914080 | 00:00 |
| 12 | 0.473769 | 0.442921 | 0.917629 | 00:00 |
| 13 | 0.457142 | 0.441750 | 0.918359 | 00:00 |
| 14 | 0.448581 | 0.436101 | 0.918597 | 00:00 |
| 15 | 0.444531 | 0.440070 | 0.918699 | 00:00 |
| 16 | 0.441617 | 0.440377 | 0.918716 | 00:00 |
| 17 | 0.439767 | 0.436971 | 0.918597 | 00:00 |
| 18 | 0.438270 | 0.435470 | 0.918716 | 00:00 |
| 19 | 0.437275 | 0.432478 | 0.918716 | 00:00 |
| 20 | 0.435754 | 0.436174 | 0.918546 | 00:00 |
| 21 | 0.434826 | 0.435958 | 0.916508 | 00:00 |
| 22 | 0.434420 | 0.429394 | 0.918648 | 00:00 |
| 23 | 0.433856 | 0.430365 | 0.916508 | 00:00 |
| 24 | 0.433128 | 0.429061 | 0.918750 | 00:00 |
| 25 | 0.432005 | 0.430671 | 0.918852 | 00:00 |
| 26 | 0.430558 | 0.428604 | 0.918733 | 00:00 |
| 27 | 0.429318 | 0.426835 | 0.918818 | 00:00 |
| 28 | 0.428890 | 0.425801 | 0.918784 | 00:00 |
| 29 | 0.429739 | 0.426460 | 0.918750 | 00:00 |
| 30 | 0.429386 | 0.427223 | 0.918784 | 00:00 |
| 31 | 0.429269 | 0.424235 | 0.918835 | 00:00 |
| 32 | 0.428167 | 0.425157 | 0.918767 | 00:00 |
| 33 | 0.427013 | 0.424845 | 0.918784 | 00:00 |
| 34 | 0.426773 | 0.424350 | 0.918716 | 00:00 |
| 35 | 0.426424 | 0.422890 | 0.918818 | 00:00 |
| 36 | 0.425107 | 0.424125 | 0.918835 | 00:00 |
| 37 | 0.424581 | 0.423103 | 0.918801 | 00:00 |
| 38 | 0.423724 | 0.421759 | 0.918801 | 00:00 |
| 39 | 0.423152 | 0.421920 | 0.918784 | 00:00 |
| 40 | 0.422518 | 0.422469 | 0.918784 | 00:00 |
| 41 | 0.422166 | 0.422614 | 0.918869 | 00:00 |
| 42 | 0.421871 | 0.421643 | 0.918818 | 00:00 |
| 43 | 0.422184 | 0.421409 | 0.918852 | 00:00 |
| 44 | 0.422510 | 0.421301 | 0.918852 | 00:00 |
| 45 | 0.421371 | 0.421397 | 0.918818 | 00:00 |
| 46 | 0.421157 | 0.421217 | 0.918784 | 00:00 |
| 47 | 0.420860 | 0.421129 | 0.918818 | 00:00 |
| 48 | 0.420694 | 0.421030 | 0.918835 | 00:00 |
| 49 | 0.420269 | 0.421029 | 0.918852 | 00:00 |
| 50 | 0.420817 | 0.421000 | 0.918801 | 00:00 |
Paths: 3058 , Tokens: 1372 [36, 64, 181, 1003, 1063, 1335] Adj. Rand Index Score: 0.951012. Adj. Mutual Info Score: 0.909999. Classes: [0. 1.] n_clusters: 2.0 Seed: 987654321
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 7.058703 | 6.920393 | 0.047894 | 00:00 |
| 2 | 6.707454 | 6.293720 | 0.310971 | 00:00 |
| 3 | 6.084228 | 5.188914 | 0.522656 | 00:00 |
| 4 | 4.905699 | 3.309880 | 0.629586 | 00:00 |
| 5 | 3.371505 | 1.825627 | 0.729008 | 00:00 |
| 6 | 2.178928 | 1.140837 | 0.816151 | 00:00 |
| 7 | 1.411689 | 0.771000 | 0.872775 | 00:00 |
| 8 | 0.960166 | 0.580654 | 0.900289 | 00:00 |
| 9 | 0.708822 | 0.501180 | 0.911583 | 00:00 |
| 10 | 0.576291 | 0.473657 | 0.914198 | 00:00 |
| 11 | 0.506285 | 0.460301 | 0.918071 | 00:00 |
| 12 | 0.471464 | 0.447850 | 0.918359 | 00:00 |
| 13 | 0.457254 | 0.444729 | 0.918597 | 00:00 |
| 14 | 0.447793 | 0.443605 | 0.916780 | 00:00 |
| 15 | 0.444058 | 0.441806 | 0.918563 | 00:00 |
| 16 | 0.441777 | 0.434802 | 0.918648 | 00:00 |
| 17 | 0.439698 | 0.439796 | 0.918088 | 00:00 |
| 18 | 0.437731 | 0.438012 | 0.918733 | 00:00 |
| 19 | 0.436346 | 0.433271 | 0.918784 | 00:00 |
| 20 | 0.437246 | 0.435797 | 0.918716 | 00:00 |
| 21 | 0.436527 | 0.440247 | 0.914351 | 00:00 |
| 22 | 0.433979 | 0.431665 | 0.918105 | 00:00 |
| 23 | 0.433636 | 0.431824 | 0.918750 | 00:00 |
| 24 | 0.433501 | 0.432018 | 0.918750 | 00:00 |
| 25 | 0.433385 | 0.430475 | 0.915948 | 00:00 |
| 26 | 0.432020 | 0.428339 | 0.918699 | 00:00 |
| 27 | 0.430848 | 0.431205 | 0.918733 | 00:00 |
| 28 | 0.430101 | 0.430657 | 0.914351 | 00:00 |
| 29 | 0.430049 | 0.428840 | 0.918682 | 00:00 |
| 30 | 0.428506 | 0.426172 | 0.918733 | 00:00 |
| 31 | 0.428360 | 0.428847 | 0.918852 | 00:00 |
| 32 | 0.427468 | 0.426117 | 0.916406 | 00:00 |
| 33 | 0.425787 | 0.425045 | 0.918733 | 00:00 |
| 34 | 0.426417 | 0.425037 | 0.918716 | 00:00 |
| 35 | 0.425530 | 0.424351 | 0.918784 | 00:00 |
| 36 | 0.427194 | 0.424430 | 0.918852 | 00:00 |
| 37 | 0.425410 | 0.424045 | 0.918767 | 00:00 |
| 38 | 0.425238 | 0.423300 | 0.918801 | 00:00 |
| 39 | 0.424707 | 0.423453 | 0.918869 | 00:00 |
| 40 | 0.424697 | 0.423016 | 0.918818 | 00:00 |
| 41 | 0.423454 | 0.421948 | 0.918784 | 00:00 |
| 42 | 0.423736 | 0.421850 | 0.918886 | 00:00 |
| 43 | 0.424083 | 0.421327 | 0.918852 | 00:00 |
| 44 | 0.422505 | 0.421106 | 0.918784 | 00:00 |
| 45 | 0.422093 | 0.420990 | 0.918750 | 00:00 |
| 46 | 0.422140 | 0.420956 | 0.918733 | 00:00 |
| 47 | 0.421751 | 0.420873 | 0.918801 | 00:00 |
| 48 | 0.421466 | 0.420797 | 0.918886 | 00:00 |
| 49 | 0.421029 | 0.420777 | 0.918869 | 00:00 |
| 50 | 0.420594 | 0.420875 | 0.918801 | 00:00 |
Paths: 3058 , Tokens: 1372 [36, 64, 181, 1003, 1063, 1335] Adj. Rand Index Score: 0.951012. Adj. Mutual Info Score: 0.909999. Classes: [0. 1.] n_clusters: 2.0 Seed: 86420
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 7.059550 | 6.928452 | 0.045499 | 00:00 |
| 2 | 6.713783 | 6.309832 | 0.374643 | 00:00 |
| 3 | 6.102345 | 5.213819 | 0.546179 | 00:00 |
| 4 | 4.947294 | 3.332577 | 0.620669 | 00:00 |
| 5 | 3.404826 | 1.835117 | 0.734137 | 00:00 |
| 6 | 2.201776 | 1.136351 | 0.815370 | 00:00 |
| 7 | 1.430607 | 0.767288 | 0.873540 | 00:00 |
| 8 | 0.973591 | 0.602503 | 0.894192 | 00:00 |
| 9 | 0.716220 | 0.511154 | 0.910785 | 00:00 |
| 10 | 0.582854 | 0.467845 | 0.916151 | 00:00 |
| 11 | 0.513305 | 0.462170 | 0.918122 | 00:00 |
| 12 | 0.476099 | 0.444310 | 0.918478 | 00:00 |
| 13 | 0.459376 | 0.449026 | 0.916236 | 00:00 |
| 14 | 0.449475 | 0.440607 | 0.912721 | 00:00 |
| 15 | 0.444278 | 0.436608 | 0.918648 | 00:00 |
| 16 | 0.443413 | 0.437325 | 0.913757 | 00:00 |
| 17 | 0.440427 | 0.435298 | 0.917850 | 00:00 |
| 18 | 0.438821 | 0.433817 | 0.917697 | 00:00 |
| 19 | 0.437047 | 0.437149 | 0.918767 | 00:00 |
| 20 | 0.436670 | 0.434867 | 0.916406 | 00:00 |
| 21 | 0.434998 | 0.436465 | 0.913757 | 00:00 |
| 22 | 0.435107 | 0.430531 | 0.918699 | 00:00 |
| 23 | 0.434583 | 0.432721 | 0.916644 | 00:00 |
| 24 | 0.433000 | 0.429150 | 0.918682 | 00:00 |
| 25 | 0.431419 | 0.432223 | 0.918801 | 00:00 |
| 26 | 0.430751 | 0.427112 | 0.918733 | 00:00 |
| 27 | 0.428854 | 0.428719 | 0.918733 | 00:00 |
| 28 | 0.430212 | 0.430996 | 0.918784 | 00:00 |
| 29 | 0.429969 | 0.427184 | 0.918122 | 00:00 |
| 30 | 0.429004 | 0.426241 | 0.918767 | 00:00 |
| 31 | 0.428244 | 0.426445 | 0.918818 | 00:00 |
| 32 | 0.427956 | 0.425739 | 0.918597 | 00:00 |
| 33 | 0.427744 | 0.426530 | 0.918784 | 00:00 |
| 34 | 0.426080 | 0.424101 | 0.918716 | 00:00 |
| 35 | 0.425002 | 0.424386 | 0.918716 | 00:00 |
| 36 | 0.424890 | 0.423140 | 0.918733 | 00:00 |
| 37 | 0.424275 | 0.423109 | 0.918716 | 00:00 |
| 38 | 0.423405 | 0.423524 | 0.918801 | 00:00 |
| 39 | 0.423160 | 0.422654 | 0.918767 | 00:00 |
| 40 | 0.421670 | 0.422295 | 0.918835 | 00:00 |
| 41 | 0.422828 | 0.421711 | 0.918784 | 00:00 |
| 42 | 0.423683 | 0.421788 | 0.918767 | 00:00 |
| 43 | 0.422920 | 0.421652 | 0.918886 | 00:00 |
| 44 | 0.423356 | 0.421434 | 0.918801 | 00:00 |
| 45 | 0.423586 | 0.421401 | 0.918750 | 00:00 |
| 46 | 0.422662 | 0.421256 | 0.918784 | 00:00 |
| 47 | 0.421630 | 0.421009 | 0.918818 | 00:00 |
| 48 | 0.421153 | 0.421265 | 0.918767 | 00:00 |
| 49 | 0.421498 | 0.421013 | 0.918852 | 00:00 |
| 50 | 0.421730 | 0.421227 | 0.918852 | 00:00 |
Paths: 3058 , Tokens: 1372 [36, 64, 181, 1003, 1063, 1335] Adj. Rand Index Score: 0.951012. Adj. Mutual Info Score: 0.909999. Classes: [0. 1.] n_clusters: 2.0 File Name to Train: data_sets/00_bunch/glass/glass.csv ############################################################# HDBSCAN ############################################################# Adj. Rand Index Score: 0.283314. Adj. Mutual Info Score: 0.402715. Classes: [-1 0 1 2 3 4 5 6] n_clusters: 7 QSHIFTPP ############################################################# Adj. Rand Index Score: 0.333801. Adj. Mutual Info Score: 0.441001. Classes: [0 1 2 3 4 5] n_clusters: 6 HQSHIFT ############################################################# Adj. Rand Index Score: 0.286975. Adj. Mutual Info Score: 0.402591. Classes: [-1 0 1 2 3 4 5 6] n_clusters: 7 Seed: 0
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 5.427144 | 5.368369 | 0.002344 | 00:00 |
| 2 | 5.399401 | 5.353737 | 0.003320 | 00:00 |
| 3 | 5.365912 | 5.327244 | 0.012500 | 00:00 |
| 4 | 5.319256 | 5.279968 | 0.060938 | 00:00 |
| 5 | 5.258268 | 5.198246 | 0.160547 | 00:00 |
| 6 | 5.182064 | 5.058128 | 0.250781 | 00:00 |
| 7 | 5.087173 | 4.818050 | 0.321484 | 00:00 |
| 8 | 4.966190 | 4.416709 | 0.413281 | 00:00 |
| 9 | 4.811664 | 3.824367 | 0.492969 | 00:00 |
| 10 | 4.618668 | 3.093404 | 0.544141 | 00:00 |
| 11 | 4.386407 | 2.393784 | 0.579297 | 00:00 |
| 12 | 4.122202 | 1.909624 | 0.603125 | 00:00 |
| 13 | 3.850425 | 1.613908 | 0.637109 | 00:00 |
| 14 | 3.584978 | 1.404215 | 0.678320 | 00:00 |
| 15 | 3.334800 | 1.209343 | 0.717383 | 00:00 |
| 16 | 3.104121 | 1.085332 | 0.752148 | 00:00 |
| 17 | 2.891346 | 0.980158 | 0.765039 | 00:00 |
| 18 | 2.697430 | 0.929224 | 0.781836 | 00:00 |
| 19 | 2.521285 | 0.883328 | 0.789648 | 00:00 |
| 20 | 2.362403 | 0.862884 | 0.792969 | 00:00 |
| 21 | 2.219600 | 0.844672 | 0.789648 | 00:00 |
| 22 | 2.091628 | 0.830644 | 0.799219 | 00:00 |
| 23 | 1.976380 | 0.830674 | 0.800195 | 00:00 |
| 24 | 1.872636 | 0.818609 | 0.799414 | 00:00 |
| 25 | 1.778639 | 0.818283 | 0.800195 | 00:00 |
| 26 | 1.693414 | 0.815225 | 0.801367 | 00:00 |
| 27 | 1.617684 | 0.811447 | 0.800000 | 00:00 |
| 28 | 1.547907 | 0.810262 | 0.800195 | 00:00 |
| 29 | 1.484912 | 0.812950 | 0.800195 | 00:00 |
| 30 | 1.428019 | 0.810805 | 0.800195 | 00:00 |
| 31 | 1.375671 | 0.808737 | 0.800977 | 00:00 |
| 32 | 1.327985 | 0.802834 | 0.801562 | 00:00 |
| 33 | 1.284142 | 0.805412 | 0.800586 | 00:00 |
| 34 | 1.244037 | 0.804609 | 0.801172 | 00:00 |
| 35 | 1.207547 | 0.802274 | 0.800781 | 00:00 |
| 36 | 1.174120 | 0.801325 | 0.801953 | 00:00 |
| 37 | 1.143800 | 0.798815 | 0.801367 | 00:00 |
| 38 | 1.115263 | 0.800357 | 0.800391 | 00:00 |
| 39 | 1.089226 | 0.798268 | 0.800781 | 00:00 |
| 40 | 1.065022 | 0.799015 | 0.800586 | 00:00 |
| 41 | 1.043468 | 0.798556 | 0.801172 | 00:00 |
| 42 | 1.023291 | 0.796274 | 0.800977 | 00:00 |
| 43 | 1.005593 | 0.797120 | 0.801172 | 00:00 |
| 44 | 0.988868 | 0.797076 | 0.801172 | 00:00 |
| 45 | 0.973747 | 0.796438 | 0.802539 | 00:00 |
| 46 | 0.960024 | 0.796847 | 0.801562 | 00:00 |
| 47 | 0.947407 | 0.796639 | 0.801758 | 00:00 |
| 48 | 0.935705 | 0.796548 | 0.801953 | 00:00 |
| 49 | 0.924352 | 0.796077 | 0.801172 | 00:00 |
| 50 | 0.914348 | 0.795353 | 0.802734 | 00:00 |
/home/can/anaconda3/envs/hqshift/lib/python3.6/site-packages/fastai/datasets.py:153: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details. with open(fpath, 'r') as yaml_file: return yaml.load(yaml_file)
Paths: 567 , Tokens: 214 [30, 65, 91, 103, 131, 144, 161, 186, 205] 0 Adj. Rand Index Score: 0.294805. Adj. Mutual Info Score: 0.394123. Classes: [0. 1. 2. 3. 4. 6.] n_clusters: 7.0 Seed: 23
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 5.398248 | 5.331454 | 0.001953 | 00:00 |
| 2 | 5.369689 | 5.319881 | 0.004687 | 00:00 |
| 3 | 5.329267 | 5.295765 | 0.022656 | 00:00 |
| 4 | 5.277300 | 5.249129 | 0.079297 | 00:00 |
| 5 | 5.207437 | 5.167469 | 0.219141 | 00:00 |
| 6 | 5.120356 | 5.021542 | 0.337891 | 00:00 |
| 7 | 5.014138 | 4.761950 | 0.394141 | 00:00 |
| 8 | 4.880582 | 4.342500 | 0.473242 | 00:00 |
| 9 | 4.715627 | 3.737195 | 0.536914 | 00:00 |
| 10 | 4.513608 | 3.031506 | 0.570508 | 00:00 |
| 11 | 4.275284 | 2.367526 | 0.591992 | 00:00 |
| 12 | 4.012300 | 1.875890 | 0.625977 | 00:00 |
| 13 | 3.749454 | 1.574549 | 0.656641 | 00:00 |
| 14 | 3.494818 | 1.369565 | 0.673828 | 00:00 |
| 15 | 3.252964 | 1.208162 | 0.712305 | 00:00 |
| 16 | 3.031253 | 1.085760 | 0.754102 | 00:00 |
| 17 | 2.826158 | 1.013570 | 0.748828 | 00:00 |
| 18 | 2.639806 | 0.956116 | 0.754102 | 00:00 |
| 19 | 2.469978 | 0.902447 | 0.792773 | 00:00 |
| 20 | 2.317794 | 0.889296 | 0.782812 | 00:00 |
| 21 | 2.180147 | 0.858123 | 0.777344 | 00:00 |
| 22 | 2.056628 | 0.841688 | 0.800391 | 00:00 |
| 23 | 1.945992 | 0.829220 | 0.799414 | 00:00 |
| 24 | 1.845740 | 0.818463 | 0.800977 | 00:00 |
| 25 | 1.754671 | 0.816477 | 0.797852 | 00:00 |
| 26 | 1.672415 | 0.814316 | 0.800586 | 00:00 |
| 27 | 1.598014 | 0.809497 | 0.802344 | 00:00 |
| 28 | 1.529709 | 0.811810 | 0.800391 | 00:00 |
| 29 | 1.468987 | 0.807440 | 0.800000 | 00:00 |
| 30 | 1.413436 | 0.810472 | 0.800586 | 00:00 |
| 31 | 1.362966 | 0.805451 | 0.800977 | 00:00 |
| 32 | 1.316888 | 0.806608 | 0.800000 | 00:00 |
| 33 | 1.274596 | 0.803365 | 0.801172 | 00:00 |
| 34 | 1.235708 | 0.802979 | 0.800195 | 00:00 |
| 35 | 1.200293 | 0.802813 | 0.800586 | 00:00 |
| 36 | 1.168010 | 0.800269 | 0.800977 | 00:00 |
| 37 | 1.138070 | 0.798714 | 0.801563 | 00:00 |
| 38 | 1.111602 | 0.799756 | 0.801758 | 00:00 |
| 39 | 1.086605 | 0.797808 | 0.800000 | 00:00 |
| 40 | 1.063183 | 0.797533 | 0.802344 | 00:00 |
| 41 | 1.042333 | 0.796278 | 0.802734 | 00:00 |
| 42 | 1.023434 | 0.796351 | 0.802148 | 00:00 |
| 43 | 1.005940 | 0.796566 | 0.801563 | 00:00 |
| 44 | 0.989363 | 0.795533 | 0.800977 | 00:00 |
| 45 | 0.974159 | 0.795558 | 0.801367 | 00:00 |
| 46 | 0.960199 | 0.796038 | 0.801953 | 00:00 |
| 47 | 0.946614 | 0.794688 | 0.802344 | 00:00 |
| 48 | 0.934538 | 0.795631 | 0.801367 | 00:00 |
| 49 | 0.923215 | 0.796665 | 0.800781 | 00:00 |
| 50 | 0.913385 | 0.796368 | 0.800586 | 00:00 |
Paths: 567 , Tokens: 214 [30, 65, 91, 103, 131, 144, 161, 186, 205] 0 Adj. Rand Index Score: 0.294805. Adj. Mutual Info Score: 0.394123. Classes: [0. 1. 2. 3. 4. 6.] n_clusters: 7.0 Seed: 42
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 5.374361 | 5.378659 | 0.005078 | 00:00 |
| 2 | 5.339983 | 5.357896 | 0.007227 | 00:00 |
| 3 | 5.292035 | 5.320826 | 0.028906 | 00:00 |
| 4 | 5.230787 | 5.256152 | 0.079102 | 00:00 |
| 5 | 5.148985 | 5.145339 | 0.171875 | 00:00 |
| 6 | 5.047358 | 4.965795 | 0.281250 | 00:00 |
| 7 | 4.928289 | 4.674118 | 0.387891 | 00:00 |
| 8 | 4.781484 | 4.207337 | 0.509375 | 00:00 |
| 9 | 4.601059 | 3.499662 | 0.547852 | 00:00 |
| 10 | 4.384217 | 2.709545 | 0.581445 | 00:00 |
| 11 | 4.134220 | 2.107385 | 0.601367 | 00:00 |
| 12 | 3.868825 | 1.720106 | 0.626563 | 00:00 |
| 13 | 3.604651 | 1.472240 | 0.665430 | 00:00 |
| 14 | 3.351658 | 1.291419 | 0.713281 | 00:00 |
| 15 | 3.119538 | 1.142360 | 0.739258 | 00:00 |
| 16 | 2.903648 | 1.045710 | 0.745508 | 00:00 |
| 17 | 2.708824 | 0.978470 | 0.762305 | 00:00 |
| 18 | 2.531967 | 0.914025 | 0.787891 | 00:00 |
| 19 | 2.372274 | 0.876546 | 0.794531 | 00:00 |
| 20 | 2.227128 | 0.856778 | 0.796484 | 00:00 |
| 21 | 2.098412 | 0.852706 | 0.799414 | 00:00 |
| 22 | 1.981949 | 0.832158 | 0.796094 | 00:00 |
| 23 | 1.877553 | 0.821958 | 0.800586 | 00:00 |
| 24 | 1.783025 | 0.832075 | 0.801758 | 00:00 |
| 25 | 1.697544 | 0.819706 | 0.790234 | 00:00 |
| 26 | 1.620323 | 0.820846 | 0.800586 | 00:00 |
| 27 | 1.550885 | 0.817348 | 0.800000 | 00:00 |
| 28 | 1.487016 | 0.812146 | 0.801563 | 00:00 |
| 29 | 1.429000 | 0.807847 | 0.801172 | 00:00 |
| 30 | 1.376878 | 0.804745 | 0.801367 | 00:00 |
| 31 | 1.328821 | 0.810850 | 0.800781 | 00:00 |
| 32 | 1.285855 | 0.805254 | 0.800586 | 00:00 |
| 33 | 1.245599 | 0.803053 | 0.801172 | 00:00 |
| 34 | 1.209744 | 0.802491 | 0.801172 | 00:00 |
| 35 | 1.176989 | 0.800639 | 0.800195 | 00:00 |
| 36 | 1.146517 | 0.800478 | 0.801563 | 00:00 |
| 37 | 1.118560 | 0.799258 | 0.800586 | 00:00 |
| 38 | 1.093218 | 0.800826 | 0.800977 | 00:00 |
| 39 | 1.069428 | 0.799199 | 0.801953 | 00:00 |
| 40 | 1.047716 | 0.798319 | 0.800781 | 00:00 |
| 41 | 1.027881 | 0.795561 | 0.801367 | 00:00 |
| 42 | 1.009845 | 0.795856 | 0.800977 | 00:00 |
| 43 | 0.993027 | 0.795522 | 0.801758 | 00:00 |
| 44 | 0.977310 | 0.796876 | 0.801367 | 00:00 |
| 45 | 0.962652 | 0.796104 | 0.802148 | 00:00 |
| 46 | 0.949457 | 0.796622 | 0.801758 | 00:00 |
| 47 | 0.937269 | 0.795296 | 0.801172 | 00:00 |
| 48 | 0.926594 | 0.795738 | 0.801953 | 00:00 |
| 49 | 0.917096 | 0.795468 | 0.801172 | 00:00 |
| 50 | 0.907895 | 0.796096 | 0.801367 | 00:00 |
Paths: 567 , Tokens: 214 [30, 65, 91, 103, 131, 144, 161, 186, 205] Adj. Rand Index Score: 0.294805. Adj. Mutual Info Score: 0.394123. Classes: [0. 1. 2. 3. 4. 6.] n_clusters: 7.0 Seed: 1234
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 5.438473 | 5.382486 | 0.000977 | 00:00 |
| 2 | 5.417439 | 5.372610 | 0.002539 | 00:00 |
| 3 | 5.386519 | 5.351558 | 0.004883 | 00:00 |
| 4 | 5.343688 | 5.310810 | 0.032227 | 00:00 |
| 5 | 5.285374 | 5.235862 | 0.119727 | 00:00 |
| 6 | 5.210473 | 5.105072 | 0.234570 | 00:00 |
| 7 | 5.116263 | 4.874384 | 0.359766 | 00:00 |
| 8 | 4.999289 | 4.483549 | 0.429492 | 00:00 |
| 9 | 4.853271 | 3.910456 | 0.519922 | 00:00 |
| 10 | 4.667958 | 3.187985 | 0.540625 | 00:00 |
| 11 | 4.438830 | 2.444216 | 0.559766 | 00:00 |
| 12 | 4.178160 | 1.936840 | 0.607813 | 00:00 |
| 13 | 3.905120 | 1.637382 | 0.647656 | 00:00 |
| 14 | 3.640414 | 1.419299 | 0.683398 | 00:00 |
| 15 | 3.390141 | 1.261526 | 0.710547 | 00:00 |
| 16 | 3.155842 | 1.140978 | 0.734570 | 00:00 |
| 17 | 2.940582 | 1.015607 | 0.771680 | 00:00 |
| 18 | 2.743719 | 0.954670 | 0.771094 | 00:00 |
| 19 | 2.563874 | 0.902828 | 0.782422 | 00:00 |
| 20 | 2.402635 | 0.869242 | 0.793359 | 00:00 |
| 21 | 2.258146 | 0.871749 | 0.795898 | 00:00 |
| 22 | 2.127726 | 0.848889 | 0.783789 | 00:00 |
| 23 | 2.010966 | 0.840084 | 0.799023 | 00:00 |
| 24 | 1.904624 | 0.826150 | 0.794141 | 00:00 |
| 25 | 1.809020 | 0.822109 | 0.800195 | 00:00 |
| 26 | 1.723087 | 0.818189 | 0.801563 | 00:00 |
| 27 | 1.644708 | 0.815378 | 0.801172 | 00:00 |
| 28 | 1.572955 | 0.808805 | 0.801172 | 00:00 |
| 29 | 1.508170 | 0.809144 | 0.801172 | 00:00 |
| 30 | 1.449402 | 0.807901 | 0.799609 | 00:00 |
| 31 | 1.395845 | 0.808553 | 0.795898 | 00:00 |
| 32 | 1.346809 | 0.805604 | 0.800195 | 00:00 |
| 33 | 1.301498 | 0.805667 | 0.799805 | 00:00 |
| 34 | 1.260844 | 0.801534 | 0.801563 | 00:00 |
| 35 | 1.223333 | 0.803855 | 0.801172 | 00:00 |
| 36 | 1.188969 | 0.801589 | 0.799023 | 00:00 |
| 37 | 1.157363 | 0.800275 | 0.801172 | 00:00 |
| 38 | 1.128395 | 0.799552 | 0.800977 | 00:00 |
| 39 | 1.102524 | 0.800034 | 0.800977 | 00:00 |
| 40 | 1.077862 | 0.800371 | 0.799609 | 00:00 |
| 41 | 1.055149 | 0.799199 | 0.799414 | 00:00 |
| 42 | 1.034487 | 0.798135 | 0.800000 | 00:00 |
| 43 | 1.014956 | 0.796940 | 0.800781 | 00:00 |
| 44 | 0.998750 | 0.796775 | 0.801563 | 00:00 |
| 45 | 0.982787 | 0.796935 | 0.800781 | 00:00 |
| 46 | 0.967706 | 0.797409 | 0.799805 | 00:00 |
| 47 | 0.953863 | 0.797416 | 0.799805 | 00:00 |
| 48 | 0.941485 | 0.796850 | 0.800391 | 00:00 |
| 49 | 0.930310 | 0.796693 | 0.799805 | 00:00 |
| 50 | 0.919816 | 0.796753 | 0.799414 | 00:00 |
Paths: 567 , Tokens: 214 [30, 65, 91, 103, 131, 144, 161, 186, 205] 0 Adj. Rand Index Score: 0.294805. Adj. Mutual Info Score: 0.394123. Classes: [0. 1. 2. 3. 4. 6.] n_clusters: 7.0 Seed: 43210
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 5.415949 | 5.372629 | 0.001563 | 00:00 |
| 2 | 5.387386 | 5.356244 | 0.001758 | 00:00 |
| 3 | 5.350883 | 5.326912 | 0.003711 | 00:00 |
| 4 | 5.300557 | 5.274119 | 0.052539 | 00:00 |
| 5 | 5.235893 | 5.180200 | 0.166602 | 00:00 |
| 6 | 5.154415 | 5.020784 | 0.235547 | 00:00 |
| 7 | 5.056523 | 4.765321 | 0.393945 | 00:00 |
| 8 | 4.934827 | 4.379003 | 0.467578 | 00:00 |
| 9 | 4.779448 | 3.792989 | 0.510156 | 00:00 |
| 10 | 4.584645 | 3.064451 | 0.558398 | 00:00 |
| 11 | 4.356037 | 2.421943 | 0.584766 | 00:00 |
| 12 | 4.101272 | 1.968108 | 0.612695 | 00:00 |
| 13 | 3.837967 | 1.625907 | 0.659766 | 00:00 |
| 14 | 3.577778 | 1.374960 | 0.694727 | 00:00 |
| 15 | 3.330235 | 1.201552 | 0.729492 | 00:00 |
| 16 | 3.099359 | 1.081877 | 0.746289 | 00:00 |
| 17 | 2.887284 | 0.988439 | 0.759375 | 00:00 |
| 18 | 2.693635 | 0.942499 | 0.771484 | 00:00 |
| 19 | 2.517783 | 0.906363 | 0.781055 | 00:00 |
| 20 | 2.359926 | 0.863814 | 0.798047 | 00:00 |
| 21 | 2.217594 | 0.854104 | 0.795508 | 00:00 |
| 22 | 2.089619 | 0.836234 | 0.789844 | 00:00 |
| 23 | 1.974926 | 0.825812 | 0.800000 | 00:00 |
| 24 | 1.871379 | 0.821260 | 0.793750 | 00:00 |
| 25 | 1.777496 | 0.815331 | 0.799609 | 00:00 |
| 26 | 1.693822 | 0.814534 | 0.800000 | 00:00 |
| 27 | 1.617368 | 0.807694 | 0.801172 | 00:00 |
| 28 | 1.547397 | 0.810921 | 0.800195 | 00:00 |
| 29 | 1.485135 | 0.806175 | 0.801172 | 00:00 |
| 30 | 1.427836 | 0.808170 | 0.801172 | 00:00 |
| 31 | 1.376165 | 0.805212 | 0.800781 | 00:00 |
| 32 | 1.328946 | 0.803677 | 0.800977 | 00:00 |
| 33 | 1.284955 | 0.804756 | 0.797070 | 00:00 |
| 34 | 1.245515 | 0.803555 | 0.800195 | 00:00 |
| 35 | 1.209459 | 0.802104 | 0.801367 | 00:00 |
| 36 | 1.176084 | 0.799711 | 0.800195 | 00:00 |
| 37 | 1.145829 | 0.799598 | 0.800977 | 00:00 |
| 38 | 1.118161 | 0.798446 | 0.800781 | 00:00 |
| 39 | 1.092801 | 0.796618 | 0.800586 | 00:00 |
| 40 | 1.068562 | 0.797521 | 0.800586 | 00:00 |
| 41 | 1.047298 | 0.795061 | 0.801172 | 00:00 |
| 42 | 1.027468 | 0.796186 | 0.800586 | 00:00 |
| 43 | 1.009327 | 0.794895 | 0.801563 | 00:00 |
| 44 | 0.992661 | 0.795217 | 0.801172 | 00:00 |
| 45 | 0.976971 | 0.794370 | 0.802539 | 00:00 |
| 46 | 0.962707 | 0.794178 | 0.802539 | 00:00 |
| 47 | 0.949888 | 0.794305 | 0.800781 | 00:00 |
| 48 | 0.937328 | 0.794549 | 0.800391 | 00:00 |
| 49 | 0.925604 | 0.794452 | 0.800586 | 00:00 |
| 50 | 0.915789 | 0.794068 | 0.800781 | 00:00 |
Paths: 567 , Tokens: 214 [30, 65, 91, 103, 131, 144, 161, 186, 205] 0 Adj. Rand Index Score: 0.294805. Adj. Mutual Info Score: 0.394123. Classes: [0. 1. 2. 3. 4. 6.] n_clusters: 7.0 Seed: 1133557799
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 5.445707 | 5.404941 | 0.003516 | 00:00 |
| 2 | 5.419288 | 5.391619 | 0.010938 | 00:00 |
| 3 | 5.387238 | 5.367760 | 0.012695 | 00:00 |
| 4 | 5.342072 | 5.324619 | 0.030469 | 00:00 |
| 5 | 5.281643 | 5.246801 | 0.145313 | 00:00 |
| 6 | 5.206069 | 5.115916 | 0.249414 | 00:00 |
| 7 | 5.113408 | 4.890670 | 0.316211 | 00:00 |
| 8 | 4.995150 | 4.512926 | 0.462109 | 00:00 |
| 9 | 4.842824 | 3.923444 | 0.531445 | 00:00 |
| 10 | 4.649858 | 3.172139 | 0.589453 | 00:00 |
| 11 | 4.414536 | 2.453944 | 0.622852 | 00:00 |
| 12 | 4.146249 | 1.918891 | 0.638477 | 00:00 |
| 13 | 3.870106 | 1.577405 | 0.660547 | 00:00 |
| 14 | 3.599639 | 1.340352 | 0.708789 | 00:00 |
| 15 | 3.345223 | 1.189892 | 0.732031 | 00:00 |
| 16 | 3.109722 | 1.074925 | 0.745313 | 00:00 |
| 17 | 2.895240 | 0.977418 | 0.772266 | 00:00 |
| 18 | 2.700114 | 0.915190 | 0.785156 | 00:00 |
| 19 | 2.525062 | 0.882366 | 0.787109 | 00:00 |
| 20 | 2.365483 | 0.852865 | 0.797266 | 00:00 |
| 21 | 2.223419 | 0.837206 | 0.795312 | 00:00 |
| 22 | 2.094583 | 0.830641 | 0.800781 | 00:00 |
| 23 | 1.979034 | 0.825564 | 0.787891 | 00:00 |
| 24 | 1.874837 | 0.822311 | 0.790039 | 00:00 |
| 25 | 1.780612 | 0.819334 | 0.800195 | 00:00 |
| 26 | 1.695813 | 0.812046 | 0.794531 | 00:00 |
| 27 | 1.619022 | 0.807613 | 0.800391 | 00:00 |
| 28 | 1.549798 | 0.806643 | 0.800781 | 00:00 |
| 29 | 1.486589 | 0.805936 | 0.800000 | 00:00 |
| 30 | 1.428138 | 0.802629 | 0.800781 | 00:00 |
| 31 | 1.375066 | 0.803077 | 0.800781 | 00:00 |
| 32 | 1.326589 | 0.800481 | 0.801367 | 00:00 |
| 33 | 1.282177 | 0.800856 | 0.800977 | 00:00 |
| 34 | 1.242726 | 0.801639 | 0.800195 | 00:00 |
| 35 | 1.205885 | 0.800236 | 0.800391 | 00:00 |
| 36 | 1.173294 | 0.797816 | 0.800977 | 00:00 |
| 37 | 1.143243 | 0.798260 | 0.801172 | 00:00 |
| 38 | 1.114828 | 0.796272 | 0.801563 | 00:00 |
| 39 | 1.089547 | 0.797639 | 0.800977 | 00:00 |
| 40 | 1.065755 | 0.795402 | 0.801367 | 00:00 |
| 41 | 1.044314 | 0.794953 | 0.800391 | 00:00 |
| 42 | 1.024371 | 0.795076 | 0.801562 | 00:00 |
| 43 | 1.005339 | 0.795108 | 0.801172 | 00:00 |
| 44 | 0.988192 | 0.794587 | 0.800586 | 00:00 |
| 45 | 0.972780 | 0.794895 | 0.801367 | 00:00 |
| 46 | 0.958902 | 0.795103 | 0.801172 | 00:00 |
| 47 | 0.946486 | 0.794446 | 0.802148 | 00:00 |
| 48 | 0.934472 | 0.794027 | 0.801758 | 00:00 |
| 49 | 0.923011 | 0.794338 | 0.801172 | 00:00 |
| 50 | 0.912894 | 0.794236 | 0.801563 | 00:00 |
Paths: 567 , Tokens: 214 [30, 65, 91, 103, 131, 144, 161, 186, 205] 0 Adj. Rand Index Score: 0.294805. Adj. Mutual Info Score: 0.394123. Classes: [0. 1. 2. 3. 4. 6.] n_clusters: 7.0 Seed: 22446688
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 5.432507 | 5.368482 | 0.002148 | 00:00 |
| 2 | 5.403538 | 5.354968 | 0.005859 | 00:00 |
| 3 | 5.364209 | 5.326710 | 0.009180 | 00:00 |
| 4 | 5.312761 | 5.274282 | 0.016406 | 00:00 |
| 5 | 5.246958 | 5.181679 | 0.090625 | 00:00 |
| 6 | 5.163605 | 5.027995 | 0.248828 | 00:00 |
| 7 | 5.062183 | 4.773528 | 0.367383 | 00:00 |
| 8 | 4.934366 | 4.358638 | 0.454883 | 00:00 |
| 9 | 4.772567 | 3.746915 | 0.532227 | 00:00 |
| 10 | 4.573374 | 3.000885 | 0.584961 | 00:00 |
| 11 | 4.335189 | 2.311109 | 0.617188 | 00:00 |
| 12 | 4.072542 | 1.834321 | 0.635742 | 00:00 |
| 13 | 3.800724 | 1.540442 | 0.660547 | 00:00 |
| 14 | 3.536475 | 1.321257 | 0.704297 | 00:00 |
| 15 | 3.290630 | 1.182073 | 0.730469 | 00:00 |
| 16 | 3.061923 | 1.064225 | 0.743359 | 00:00 |
| 17 | 2.853572 | 0.983672 | 0.774414 | 00:00 |
| 18 | 2.664521 | 0.942278 | 0.775000 | 00:00 |
| 19 | 2.492921 | 0.899311 | 0.781641 | 00:00 |
| 20 | 2.336987 | 0.875144 | 0.792969 | 00:00 |
| 21 | 2.198127 | 0.848582 | 0.798242 | 00:00 |
| 22 | 2.072954 | 0.840340 | 0.798438 | 00:00 |
| 23 | 1.960207 | 0.830013 | 0.791016 | 00:00 |
| 24 | 1.858200 | 0.823771 | 0.799219 | 00:00 |
| 25 | 1.766188 | 0.819789 | 0.796289 | 00:00 |
| 26 | 1.683505 | 0.813071 | 0.800586 | 00:00 |
| 27 | 1.607569 | 0.811530 | 0.800781 | 00:00 |
| 28 | 1.539718 | 0.809159 | 0.798828 | 00:00 |
| 29 | 1.477788 | 0.808614 | 0.800000 | 00:00 |
| 30 | 1.421047 | 0.808183 | 0.799609 | 00:00 |
| 31 | 1.368896 | 0.804954 | 0.800586 | 00:00 |
| 32 | 1.321630 | 0.803534 | 0.800977 | 00:00 |
| 33 | 1.278513 | 0.801797 | 0.800195 | 00:00 |
| 34 | 1.238955 | 0.800470 | 0.801758 | 00:00 |
| 35 | 1.202372 | 0.802020 | 0.800977 | 00:00 |
| 36 | 1.170228 | 0.799871 | 0.800977 | 00:00 |
| 37 | 1.140174 | 0.798878 | 0.800195 | 00:00 |
| 38 | 1.112306 | 0.798711 | 0.800977 | 00:00 |
| 39 | 1.087047 | 0.797429 | 0.801172 | 00:00 |
| 40 | 1.063341 | 0.797981 | 0.800781 | 00:00 |
| 41 | 1.042231 | 0.796601 | 0.801367 | 00:00 |
| 42 | 1.022938 | 0.796355 | 0.800977 | 00:00 |
| 43 | 1.004754 | 0.795888 | 0.800586 | 00:00 |
| 44 | 0.988155 | 0.794983 | 0.799805 | 00:00 |
| 45 | 0.972633 | 0.794999 | 0.800586 | 00:00 |
| 46 | 0.958317 | 0.795625 | 0.800977 | 00:00 |
| 47 | 0.945728 | 0.794926 | 0.801953 | 00:00 |
| 48 | 0.934246 | 0.795342 | 0.800000 | 00:00 |
| 49 | 0.923352 | 0.794570 | 0.800781 | 00:00 |
| 50 | 0.913195 | 0.796322 | 0.799609 | 00:00 |
Paths: 567 , Tokens: 214 [30, 65, 91, 103, 131, 144, 161, 186, 205] 0 Adj. Rand Index Score: 0.294805. Adj. Mutual Info Score: 0.394123. Classes: [0. 1. 2. 3. 4. 6.] n_clusters: 7.0 Seed: 123456789
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 5.418828 | 5.368891 | 0.012891 | 00:00 |
| 2 | 5.390048 | 5.354771 | 0.018359 | 00:00 |
| 3 | 5.352821 | 5.327748 | 0.039453 | 00:00 |
| 4 | 5.300632 | 5.277109 | 0.079297 | 00:00 |
| 5 | 5.231560 | 5.187832 | 0.141797 | 00:00 |
| 6 | 5.144809 | 5.034313 | 0.261914 | 00:00 |
| 7 | 5.038313 | 4.778243 | 0.375781 | 00:00 |
| 8 | 4.907612 | 4.372129 | 0.455469 | 00:00 |
| 9 | 4.743863 | 3.780737 | 0.529102 | 00:00 |
| 10 | 4.542942 | 3.050423 | 0.575391 | 00:00 |
| 11 | 4.306057 | 2.362485 | 0.600195 | 00:00 |
| 12 | 4.042459 | 1.851825 | 0.626172 | 00:00 |
| 13 | 3.774402 | 1.533700 | 0.675391 | 00:00 |
| 14 | 3.512177 | 1.289260 | 0.713867 | 00:00 |
| 15 | 3.264300 | 1.159420 | 0.733203 | 00:00 |
| 16 | 3.035799 | 1.033432 | 0.755078 | 00:00 |
| 17 | 2.827238 | 0.980518 | 0.765234 | 00:00 |
| 18 | 2.637195 | 0.928460 | 0.766797 | 00:00 |
| 19 | 2.466940 | 0.905572 | 0.784766 | 00:00 |
| 20 | 2.312842 | 0.873604 | 0.776367 | 00:00 |
| 21 | 2.174833 | 0.843754 | 0.799219 | 00:00 |
| 22 | 2.050662 | 0.833562 | 0.784180 | 00:00 |
| 23 | 1.938625 | 0.822558 | 0.799219 | 00:00 |
| 24 | 1.837752 | 0.816449 | 0.800000 | 00:00 |
| 25 | 1.747066 | 0.811970 | 0.795508 | 00:00 |
| 26 | 1.665430 | 0.814990 | 0.798438 | 00:00 |
| 27 | 1.591843 | 0.813639 | 0.800977 | 00:00 |
| 28 | 1.524798 | 0.808399 | 0.801758 | 00:00 |
| 29 | 1.464110 | 0.809906 | 0.799609 | 00:00 |
| 30 | 1.408702 | 0.804273 | 0.799414 | 00:00 |
| 31 | 1.357911 | 0.802864 | 0.799805 | 00:00 |
| 32 | 1.311893 | 0.804022 | 0.800000 | 00:00 |
| 33 | 1.269561 | 0.800617 | 0.800000 | 00:00 |
| 34 | 1.231596 | 0.799093 | 0.800195 | 00:00 |
| 35 | 1.195944 | 0.800463 | 0.800391 | 00:00 |
| 36 | 1.163902 | 0.798366 | 0.800195 | 00:00 |
| 37 | 1.134108 | 0.798747 | 0.800781 | 00:00 |
| 38 | 1.107098 | 0.797464 | 0.801172 | 00:00 |
| 39 | 1.081676 | 0.796666 | 0.801367 | 00:00 |
| 40 | 1.059233 | 0.796176 | 0.800781 | 00:00 |
| 41 | 1.038508 | 0.795301 | 0.800977 | 00:00 |
| 42 | 1.018599 | 0.795221 | 0.800195 | 00:00 |
| 43 | 1.000575 | 0.794969 | 0.799219 | 00:00 |
| 44 | 0.984284 | 0.794186 | 0.800977 | 00:00 |
| 45 | 0.969127 | 0.794456 | 0.800977 | 00:00 |
| 46 | 0.955541 | 0.794302 | 0.800781 | 00:00 |
| 47 | 0.942721 | 0.794317 | 0.800781 | 00:00 |
| 48 | 0.931369 | 0.794775 | 0.800586 | 00:00 |
| 49 | 0.921002 | 0.793508 | 0.801367 | 00:00 |
| 50 | 0.911263 | 0.794619 | 0.800977 | 00:00 |
Paths: 567 , Tokens: 214 [30, 65, 91, 103, 131, 144, 161, 186, 205] 0 Adj. Rand Index Score: 0.294805. Adj. Mutual Info Score: 0.394123. Classes: [0. 1. 2. 3. 4. 6.] n_clusters: 7.0 Seed: 987654321
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 5.397236 | 5.358831 | 0.018555 | 00:00 |
| 2 | 5.368382 | 5.345411 | 0.021875 | 00:00 |
| 3 | 5.330151 | 5.317958 | 0.037891 | 00:00 |
| 4 | 5.275702 | 5.264295 | 0.083398 | 00:00 |
| 5 | 5.207289 | 5.167618 | 0.198242 | 00:00 |
| 6 | 5.120462 | 5.002195 | 0.306055 | 00:00 |
| 7 | 5.014652 | 4.728115 | 0.387305 | 00:00 |
| 8 | 4.880757 | 4.292603 | 0.481250 | 00:00 |
| 9 | 4.711308 | 3.639076 | 0.542578 | 00:00 |
| 10 | 4.505122 | 2.885981 | 0.573633 | 00:00 |
| 11 | 4.262777 | 2.244725 | 0.599219 | 00:00 |
| 12 | 3.997744 | 1.833872 | 0.611133 | 00:00 |
| 13 | 3.729007 | 1.549649 | 0.647852 | 00:00 |
| 14 | 3.468719 | 1.317632 | 0.701758 | 00:00 |
| 15 | 3.227343 | 1.141816 | 0.735352 | 00:00 |
| 16 | 3.002196 | 1.028745 | 0.754102 | 00:00 |
| 17 | 2.796268 | 0.960370 | 0.779297 | 00:00 |
| 18 | 2.609006 | 0.908250 | 0.789844 | 00:00 |
| 19 | 2.440930 | 0.885023 | 0.794336 | 00:00 |
| 20 | 2.289992 | 0.851916 | 0.793164 | 00:00 |
| 21 | 2.153936 | 0.842921 | 0.790039 | 00:00 |
| 22 | 2.031816 | 0.845770 | 0.799805 | 00:00 |
| 23 | 1.922322 | 0.832217 | 0.778516 | 00:00 |
| 24 | 1.824376 | 0.822419 | 0.800586 | 00:00 |
| 25 | 1.735398 | 0.822433 | 0.801172 | 00:00 |
| 26 | 1.654452 | 0.818762 | 0.796484 | 00:00 |
| 27 | 1.581036 | 0.814686 | 0.800195 | 00:00 |
| 28 | 1.514118 | 0.815182 | 0.801562 | 00:00 |
| 29 | 1.453268 | 0.808061 | 0.802344 | 00:00 |
| 30 | 1.398583 | 0.809029 | 0.801172 | 00:00 |
| 31 | 1.348803 | 0.804467 | 0.800977 | 00:00 |
| 32 | 1.303195 | 0.802211 | 0.801172 | 00:00 |
| 33 | 1.261931 | 0.800696 | 0.801367 | 00:00 |
| 34 | 1.224258 | 0.798310 | 0.801953 | 00:00 |
| 35 | 1.189537 | 0.800376 | 0.801758 | 00:00 |
| 36 | 1.157730 | 0.800444 | 0.800391 | 00:00 |
| 37 | 1.128529 | 0.799244 | 0.801758 | 00:00 |
| 38 | 1.101480 | 0.798360 | 0.800781 | 00:00 |
| 39 | 1.077014 | 0.797033 | 0.801367 | 00:00 |
| 40 | 1.054189 | 0.796945 | 0.801172 | 00:00 |
| 41 | 1.033800 | 0.796797 | 0.800781 | 00:00 |
| 42 | 1.015005 | 0.796169 | 0.800781 | 00:00 |
| 43 | 0.997927 | 0.796221 | 0.800000 | 00:00 |
| 44 | 0.981602 | 0.796247 | 0.799805 | 00:00 |
| 45 | 0.967038 | 0.795532 | 0.800781 | 00:00 |
| 46 | 0.953310 | 0.794934 | 0.801172 | 00:00 |
| 47 | 0.941302 | 0.794185 | 0.801172 | 00:00 |
| 48 | 0.929382 | 0.795850 | 0.801172 | 00:00 |
| 49 | 0.919317 | 0.794937 | 0.801563 | 00:00 |
| 50 | 0.909220 | 0.794462 | 0.801367 | 00:00 |
Paths: 567 , Tokens: 214 [30, 65, 91, 103, 131, 144, 161, 186, 205] 0 Adj. Rand Index Score: 0.294805. Adj. Mutual Info Score: 0.394123. Classes: [0. 1. 2. 3. 4. 6.] n_clusters: 7.0 Seed: 86420
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 5.407234 | 5.359961 | 0.014062 | 00:00 |
| 2 | 5.381419 | 5.347956 | 0.015430 | 00:00 |
| 3 | 5.347782 | 5.323798 | 0.026953 | 00:00 |
| 4 | 5.302121 | 5.277963 | 0.058594 | 00:00 |
| 5 | 5.243896 | 5.197117 | 0.175977 | 00:00 |
| 6 | 5.170649 | 5.058437 | 0.279102 | 00:00 |
| 7 | 5.080173 | 4.834910 | 0.368750 | 00:00 |
| 8 | 4.965621 | 4.476650 | 0.424219 | 00:00 |
| 9 | 4.820881 | 3.942787 | 0.462695 | 00:00 |
| 10 | 4.638288 | 3.250445 | 0.540820 | 00:00 |
| 11 | 4.414824 | 2.532573 | 0.585352 | 00:00 |
| 12 | 4.158218 | 1.973486 | 0.627734 | 00:00 |
| 13 | 3.889092 | 1.613008 | 0.657813 | 00:00 |
| 14 | 3.623730 | 1.371463 | 0.688086 | 00:00 |
| 15 | 3.369822 | 1.199490 | 0.729102 | 00:00 |
| 16 | 3.134225 | 1.074278 | 0.751953 | 00:00 |
| 17 | 2.917390 | 0.983997 | 0.761914 | 00:00 |
| 18 | 2.719469 | 0.952116 | 0.766211 | 00:00 |
| 19 | 2.542233 | 0.887394 | 0.788477 | 00:00 |
| 20 | 2.381347 | 0.863327 | 0.791797 | 00:00 |
| 21 | 2.236998 | 0.850426 | 0.779883 | 00:00 |
| 22 | 2.107556 | 0.830791 | 0.800391 | 00:00 |
| 23 | 1.992019 | 0.835173 | 0.798828 | 00:00 |
| 24 | 1.887831 | 0.829078 | 0.794727 | 00:00 |
| 25 | 1.793088 | 0.819388 | 0.800781 | 00:00 |
| 26 | 1.707583 | 0.811581 | 0.801172 | 00:00 |
| 27 | 1.630101 | 0.811885 | 0.800977 | 00:00 |
| 28 | 1.559145 | 0.810229 | 0.800391 | 00:00 |
| 29 | 1.494647 | 0.807598 | 0.800195 | 00:00 |
| 30 | 1.436920 | 0.806971 | 0.801367 | 00:00 |
| 31 | 1.385030 | 0.806527 | 0.799805 | 00:00 |
| 32 | 1.336386 | 0.804199 | 0.800391 | 00:00 |
| 33 | 1.292697 | 0.801871 | 0.800977 | 00:00 |
| 34 | 1.252198 | 0.804008 | 0.800977 | 00:00 |
| 35 | 1.215796 | 0.800028 | 0.801563 | 00:00 |
| 36 | 1.182603 | 0.800822 | 0.800586 | 00:00 |
| 37 | 1.151228 | 0.797123 | 0.801172 | 00:00 |
| 38 | 1.122481 | 0.796442 | 0.801367 | 00:00 |
| 39 | 1.096907 | 0.795317 | 0.801367 | 00:00 |
| 40 | 1.073471 | 0.795591 | 0.800781 | 00:00 |
| 41 | 1.051231 | 0.795421 | 0.800781 | 00:00 |
| 42 | 1.031033 | 0.795127 | 0.800977 | 00:00 |
| 43 | 1.012427 | 0.793929 | 0.801367 | 00:00 |
| 44 | 0.995373 | 0.794554 | 0.800000 | 00:00 |
| 45 | 0.979965 | 0.794341 | 0.800977 | 00:00 |
| 46 | 0.966091 | 0.793880 | 0.800195 | 00:00 |
| 47 | 0.952607 | 0.794075 | 0.800781 | 00:00 |
| 48 | 0.940047 | 0.793610 | 0.801172 | 00:00 |
| 49 | 0.928344 | 0.793673 | 0.800781 | 00:00 |
| 50 | 0.917680 | 0.793542 | 0.800977 | 00:00 |
Paths: 567 , Tokens: 214 [30, 65, 91, 103, 131, 144, 161, 186, 205] 0 Adj. Rand Index Score: 0.294805. Adj. Mutual Info Score: 0.394123. Classes: [0. 1. 2. 3. 4. 6.] n_clusters: 7.0 File Name to Train: data_sets/00_bunch/iris/iris.csv ############################################################# HDBSCAN ############################################################# Adj. Rand Index Score: 0.539409. Adj. Mutual Info Score: 0.547691. Classes: [-1 0 1] n_clusters: 2 QSHIFTPP ############################################################# Adj. Rand Index Score: 0.568116. Adj. Mutual Info Score: 0.576771. Classes: [0 1] n_clusters: 2 HQSHIFT ############################################################# Adj. Rand Index Score: 0.568116. Adj. Mutual Info Score: 0.576771. Classes: [0 1] n_clusters: 2 Seed: 0
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 5.053304 | 4.988181 | 0.001563 | 00:00 |
| 2 | 5.025689 | 4.976361 | 0.006510 | 00:00 |
| 3 | 4.992795 | 4.954509 | 0.027344 | 00:00 |
| 4 | 4.943776 | 4.915385 | 0.126823 | 00:00 |
| 5 | 4.881144 | 4.849856 | 0.191927 | 00:00 |
| 6 | 4.805586 | 4.745351 | 0.274479 | 00:00 |
| 7 | 4.716297 | 4.583688 | 0.338281 | 00:00 |
| 8 | 4.610381 | 4.338599 | 0.405469 | 00:00 |
| 9 | 4.486489 | 3.985866 | 0.451302 | 00:00 |
| 10 | 4.339971 | 3.512516 | 0.541667 | 00:00 |
| 11 | 4.170335 | 2.932702 | 0.591406 | 00:00 |
| 12 | 3.979907 | 2.330271 | 0.618229 | 00:00 |
| 13 | 3.775810 | 1.874463 | 0.643229 | 00:00 |
| 14 | 3.563110 | 1.549128 | 0.671354 | 00:00 |
| 15 | 3.355114 | 1.331996 | 0.701302 | 00:00 |
| 16 | 3.156509 | 1.166981 | 0.727083 | 00:00 |
| 17 | 2.969527 | 1.040757 | 0.752604 | 00:00 |
| 18 | 2.795720 | 0.949988 | 0.777344 | 00:00 |
| 19 | 2.635508 | 0.874407 | 0.795313 | 00:00 |
| 20 | 2.486821 | 0.827717 | 0.803385 | 00:00 |
| 21 | 2.350394 | 0.793233 | 0.809375 | 00:00 |
| 22 | 2.226846 | 0.767423 | 0.811198 | 00:00 |
| 23 | 2.112904 | 0.754300 | 0.811719 | 00:00 |
| 24 | 2.007451 | 0.742568 | 0.812760 | 00:00 |
| 25 | 1.911797 | 0.737257 | 0.813021 | 00:00 |
| 26 | 1.824705 | 0.730928 | 0.812760 | 00:00 |
| 27 | 1.745300 | 0.726970 | 0.813542 | 00:00 |
| 28 | 1.672464 | 0.724646 | 0.814323 | 00:00 |
| 29 | 1.604390 | 0.722283 | 0.815104 | 00:00 |
| 30 | 1.542273 | 0.720453 | 0.814063 | 00:00 |
| 31 | 1.485147 | 0.721667 | 0.815104 | 00:00 |
| 32 | 1.432332 | 0.719225 | 0.815365 | 00:00 |
| 33 | 1.384411 | 0.717367 | 0.813021 | 00:00 |
| 34 | 1.339536 | 0.718429 | 0.815365 | 00:00 |
| 35 | 1.297866 | 0.715540 | 0.812500 | 00:00 |
| 36 | 1.258580 | 0.714755 | 0.814323 | 00:00 |
| 37 | 1.222965 | 0.715073 | 0.813021 | 00:00 |
| 38 | 1.188933 | 0.715561 | 0.814844 | 00:00 |
| 39 | 1.157756 | 0.713416 | 0.814063 | 00:00 |
| 40 | 1.128789 | 0.713249 | 0.815104 | 00:00 |
| 41 | 1.101854 | 0.713057 | 0.813021 | 00:00 |
| 42 | 1.076556 | 0.712578 | 0.813802 | 00:00 |
| 43 | 1.053882 | 0.712590 | 0.813802 | 00:00 |
| 44 | 1.031641 | 0.714457 | 0.813281 | 00:00 |
| 45 | 1.011611 | 0.711717 | 0.813542 | 00:00 |
| 46 | 0.993231 | 0.712175 | 0.814063 | 00:00 |
| 47 | 0.975510 | 0.711659 | 0.813542 | 00:00 |
| 48 | 0.959973 | 0.711150 | 0.814063 | 00:00 |
| 49 | 0.944979 | 0.712483 | 0.813542 | 00:00 |
| 50 | 0.931133 | 0.712051 | 0.813281 | 00:00 |
/home/can/anaconda3/envs/hqshift/lib/python3.6/site-packages/fastai/datasets.py:153: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details. with open(fpath, 'r') as yaml_file: return yaml.load(yaml_file)
Paths: 318 , Tokens: 150 [8, 56, 79, 127] 0 Adj. Rand Index Score: 0.543752. Adj. Mutual Info Score: 0.542515. Classes: [0. 1.] n_clusters: 2.0 Seed: 23
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 5.019341 | 4.996638 | 0.018229 | 00:00 |
| 2 | 4.997609 | 4.987558 | 0.044271 | 00:00 |
| 3 | 4.971472 | 4.970953 | 0.063802 | 00:00 |
| 4 | 4.936319 | 4.941093 | 0.102865 | 00:00 |
| 5 | 4.888903 | 4.891029 | 0.174740 | 00:00 |
| 6 | 4.829467 | 4.809386 | 0.291406 | 00:00 |
| 7 | 4.757714 | 4.678298 | 0.387500 | 00:00 |
| 8 | 4.670977 | 4.463720 | 0.445312 | 00:00 |
| 9 | 4.564978 | 4.132545 | 0.500521 | 00:00 |
| 10 | 4.436446 | 3.649446 | 0.538542 | 00:00 |
| 11 | 4.283046 | 3.050132 | 0.579167 | 00:00 |
| 12 | 4.104758 | 2.446071 | 0.605208 | 00:00 |
| 13 | 3.903278 | 1.959281 | 0.657552 | 00:00 |
| 14 | 3.690152 | 1.606320 | 0.692708 | 00:00 |
| 15 | 3.475927 | 1.358054 | 0.712760 | 00:00 |
| 16 | 3.269978 | 1.175423 | 0.741406 | 00:00 |
| 17 | 3.075555 | 1.049262 | 0.764583 | 00:00 |
| 18 | 2.892744 | 0.950978 | 0.782813 | 00:00 |
| 19 | 2.723670 | 0.891574 | 0.791406 | 00:00 |
| 20 | 2.569546 | 0.844049 | 0.802344 | 00:00 |
| 21 | 2.426132 | 0.811740 | 0.807552 | 00:00 |
| 22 | 2.295455 | 0.780904 | 0.810156 | 00:00 |
| 23 | 2.176633 | 0.771983 | 0.810938 | 00:00 |
| 24 | 2.067152 | 0.753593 | 0.811979 | 00:00 |
| 25 | 1.969306 | 0.754714 | 0.812240 | 00:00 |
| 26 | 1.878135 | 0.739963 | 0.814063 | 00:00 |
| 27 | 1.794144 | 0.735522 | 0.814323 | 00:00 |
| 28 | 1.717579 | 0.732907 | 0.813802 | 00:00 |
| 29 | 1.646252 | 0.727329 | 0.814583 | 00:00 |
| 30 | 1.581272 | 0.723949 | 0.814063 | 00:00 |
| 31 | 1.521919 | 0.720979 | 0.814323 | 00:00 |
| 32 | 1.467096 | 0.721284 | 0.814063 | 00:00 |
| 33 | 1.416345 | 0.720118 | 0.814583 | 00:00 |
| 34 | 1.369164 | 0.716588 | 0.814063 | 00:00 |
| 35 | 1.325877 | 0.716317 | 0.814063 | 00:00 |
| 36 | 1.285874 | 0.714047 | 0.813802 | 00:00 |
| 37 | 1.248637 | 0.712475 | 0.813542 | 00:00 |
| 38 | 1.214119 | 0.712855 | 0.813802 | 00:00 |
| 39 | 1.181768 | 0.712797 | 0.814323 | 00:00 |
| 40 | 1.151778 | 0.713222 | 0.815365 | 00:00 |
| 41 | 1.123651 | 0.712171 | 0.815365 | 00:00 |
| 42 | 1.096974 | 0.710996 | 0.814844 | 00:00 |
| 43 | 1.072296 | 0.712242 | 0.813802 | 00:00 |
| 44 | 1.049550 | 0.710749 | 0.812760 | 00:00 |
| 45 | 1.028424 | 0.711791 | 0.813281 | 00:00 |
| 46 | 1.008509 | 0.711610 | 0.814063 | 00:00 |
| 47 | 0.990224 | 0.711412 | 0.813802 | 00:00 |
| 48 | 0.971730 | 0.711370 | 0.813802 | 00:00 |
| 49 | 0.955287 | 0.711508 | 0.813542 | 00:00 |
| 50 | 0.939903 | 0.710920 | 0.814583 | 00:00 |
Paths: 318 , Tokens: 150 [8, 56, 79, 127] 0 Adj. Rand Index Score: 0.543752. Adj. Mutual Info Score: 0.542515. Classes: [0. 1.] n_clusters: 2.0 Seed: 42
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 5.080227 | 5.022364 | 0.024219 | 00:00 |
| 2 | 5.057003 | 5.011244 | 0.027083 | 00:00 |
| 3 | 5.025050 | 4.990326 | 0.041927 | 00:00 |
| 4 | 4.980808 | 4.952816 | 0.048958 | 00:00 |
| 5 | 4.920654 | 4.889297 | 0.090365 | 00:00 |
| 6 | 4.850921 | 4.789301 | 0.179948 | 00:00 |
| 7 | 4.769736 | 4.631716 | 0.294531 | 00:00 |
| 8 | 4.674830 | 4.396720 | 0.410938 | 00:00 |
| 9 | 4.560826 | 4.047606 | 0.509896 | 00:00 |
| 10 | 4.422939 | 3.553832 | 0.539844 | 00:00 |
| 11 | 4.258340 | 2.951153 | 0.563281 | 00:00 |
| 12 | 4.069800 | 2.348383 | 0.597396 | 00:00 |
| 13 | 3.865042 | 1.893263 | 0.631250 | 00:00 |
| 14 | 3.654904 | 1.585672 | 0.674219 | 00:00 |
| 15 | 3.441931 | 1.350382 | 0.709896 | 00:00 |
| 16 | 3.238689 | 1.181249 | 0.736719 | 00:00 |
| 17 | 3.045974 | 1.047996 | 0.756250 | 00:00 |
| 18 | 2.865608 | 0.954951 | 0.775521 | 00:00 |
| 19 | 2.698042 | 0.877726 | 0.784635 | 00:00 |
| 20 | 2.544532 | 0.831887 | 0.797917 | 00:00 |
| 21 | 2.403277 | 0.803505 | 0.805469 | 00:00 |
| 22 | 2.274712 | 0.776508 | 0.806510 | 00:00 |
| 23 | 2.156538 | 0.760842 | 0.812760 | 00:00 |
| 24 | 2.048886 | 0.751240 | 0.813021 | 00:00 |
| 25 | 1.950785 | 0.742246 | 0.813021 | 00:00 |
| 26 | 1.861506 | 0.733962 | 0.814844 | 00:00 |
| 27 | 1.779399 | 0.728757 | 0.813542 | 00:00 |
| 28 | 1.704007 | 0.726900 | 0.814844 | 00:00 |
| 29 | 1.634964 | 0.723051 | 0.814323 | 00:00 |
| 30 | 1.571654 | 0.724257 | 0.813542 | 00:00 |
| 31 | 1.512591 | 0.721041 | 0.814583 | 00:00 |
| 32 | 1.457596 | 0.719540 | 0.814323 | 00:00 |
| 33 | 1.407585 | 0.718162 | 0.815365 | 00:00 |
| 34 | 1.361499 | 0.718239 | 0.814323 | 00:00 |
| 35 | 1.319011 | 0.718996 | 0.815365 | 00:00 |
| 36 | 1.278934 | 0.720189 | 0.815365 | 00:00 |
| 37 | 1.241441 | 0.718139 | 0.814323 | 00:00 |
| 38 | 1.206758 | 0.717821 | 0.814844 | 00:00 |
| 39 | 1.174752 | 0.715610 | 0.815625 | 00:00 |
| 40 | 1.144594 | 0.714651 | 0.814583 | 00:00 |
| 41 | 1.116919 | 0.715198 | 0.814844 | 00:00 |
| 42 | 1.091714 | 0.714607 | 0.815625 | 00:00 |
| 43 | 1.067560 | 0.715374 | 0.814323 | 00:00 |
| 44 | 1.045173 | 0.712965 | 0.816146 | 00:00 |
| 45 | 1.024142 | 0.712713 | 0.815365 | 00:00 |
| 46 | 1.004097 | 0.713506 | 0.815625 | 00:00 |
| 47 | 0.985308 | 0.712885 | 0.815885 | 00:00 |
| 48 | 0.968102 | 0.712279 | 0.814583 | 00:00 |
| 49 | 0.952933 | 0.714828 | 0.815104 | 00:00 |
| 50 | 0.938205 | 0.713824 | 0.815104 | 00:00 |
Paths: 318 , Tokens: 150 [8, 56, 79, 127] 0 Adj. Rand Index Score: 0.543752. Adj. Mutual Info Score: 0.542515. Classes: [0. 1.] n_clusters: 2.0 Seed: 1234
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 5.042325 | 5.047029 | 0.001302 | 00:00 |
| 2 | 5.022120 | 5.036441 | 0.002344 | 00:00 |
| 3 | 4.996775 | 5.017320 | 0.009896 | 00:00 |
| 4 | 4.959430 | 4.984974 | 0.024740 | 00:00 |
| 5 | 4.912694 | 4.930768 | 0.081250 | 00:00 |
| 6 | 4.853024 | 4.843086 | 0.186198 | 00:00 |
| 7 | 4.780111 | 4.706603 | 0.327083 | 00:00 |
| 8 | 4.690701 | 4.487967 | 0.429688 | 00:00 |
| 9 | 4.581708 | 4.150346 | 0.473438 | 00:00 |
| 10 | 4.448578 | 3.657698 | 0.518490 | 00:00 |
| 11 | 4.288664 | 3.045215 | 0.566406 | 00:00 |
| 12 | 4.100779 | 2.403355 | 0.621354 | 00:00 |
| 13 | 3.891055 | 1.891268 | 0.648177 | 00:00 |
| 14 | 3.673012 | 1.549185 | 0.682813 | 00:00 |
| 15 | 3.456430 | 1.316680 | 0.722396 | 00:00 |
| 16 | 3.246726 | 1.146336 | 0.750000 | 00:00 |
| 17 | 3.050423 | 1.018105 | 0.767708 | 00:00 |
| 18 | 2.868073 | 0.922228 | 0.783333 | 00:00 |
| 19 | 2.700185 | 0.859596 | 0.793490 | 00:00 |
| 20 | 2.546235 | 0.817470 | 0.806510 | 00:00 |
| 21 | 2.404366 | 0.793236 | 0.806771 | 00:00 |
| 22 | 2.274653 | 0.768772 | 0.793490 | 00:00 |
| 23 | 2.156503 | 0.752551 | 0.812500 | 00:00 |
| 24 | 2.048486 | 0.742114 | 0.813542 | 00:00 |
| 25 | 1.950352 | 0.738362 | 0.811979 | 00:00 |
| 26 | 1.860897 | 0.729546 | 0.814583 | 00:00 |
| 27 | 1.778411 | 0.728334 | 0.814063 | 00:00 |
| 28 | 1.702261 | 0.723560 | 0.814063 | 00:00 |
| 29 | 1.632258 | 0.720694 | 0.815885 | 00:00 |
| 30 | 1.568438 | 0.720170 | 0.813542 | 00:00 |
| 31 | 1.509416 | 0.719806 | 0.814583 | 00:00 |
| 32 | 1.456276 | 0.718816 | 0.814583 | 00:00 |
| 33 | 1.405846 | 0.716525 | 0.814844 | 00:00 |
| 34 | 1.359496 | 0.716177 | 0.814323 | 00:00 |
| 35 | 1.316107 | 0.717262 | 0.814323 | 00:00 |
| 36 | 1.276577 | 0.715408 | 0.814844 | 00:00 |
| 37 | 1.238863 | 0.714868 | 0.816146 | 00:00 |
| 38 | 1.205547 | 0.713977 | 0.815104 | 00:00 |
| 39 | 1.173715 | 0.713956 | 0.815365 | 00:00 |
| 40 | 1.143818 | 0.713564 | 0.814583 | 00:00 |
| 41 | 1.116458 | 0.714040 | 0.814063 | 00:00 |
| 42 | 1.090387 | 0.712152 | 0.815625 | 00:00 |
| 43 | 1.065840 | 0.712252 | 0.814323 | 00:00 |
| 44 | 1.043957 | 0.711886 | 0.814844 | 00:00 |
| 45 | 1.023331 | 0.712977 | 0.814583 | 00:00 |
| 46 | 1.003504 | 0.711233 | 0.813542 | 00:00 |
| 47 | 0.985069 | 0.710909 | 0.814063 | 00:00 |
| 48 | 0.968074 | 0.710740 | 0.814323 | 00:00 |
| 49 | 0.952053 | 0.711466 | 0.813802 | 00:00 |
| 50 | 0.936766 | 0.711831 | 0.813542 | 00:00 |
Paths: 318 , Tokens: 150 [8, 56, 79, 127] 0 Adj. Rand Index Score: 0.543752. Adj. Mutual Info Score: 0.542515. Classes: [0. 1.] n_clusters: 2.0 Seed: 43210
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 5.071328 | 4.992609 | 0.018229 | 00:00 |
| 2 | 5.043644 | 4.982543 | 0.034375 | 00:00 |
| 3 | 5.010444 | 4.964072 | 0.054948 | 00:00 |
| 4 | 4.965611 | 4.930330 | 0.076563 | 00:00 |
| 5 | 4.906096 | 4.873090 | 0.163021 | 00:00 |
| 6 | 4.831231 | 4.780248 | 0.275781 | 00:00 |
| 7 | 4.741326 | 4.633849 | 0.361979 | 00:00 |
| 8 | 4.635673 | 4.401793 | 0.394792 | 00:00 |
| 9 | 4.510111 | 4.041659 | 0.459115 | 00:00 |
| 10 | 4.365275 | 3.539735 | 0.508854 | 00:00 |
| 11 | 4.197216 | 2.950337 | 0.567448 | 00:00 |
| 12 | 4.005839 | 2.376667 | 0.617708 | 00:00 |
| 13 | 3.799995 | 1.897111 | 0.638542 | 00:00 |
| 14 | 3.587721 | 1.544759 | 0.670573 | 00:00 |
| 15 | 3.375922 | 1.306261 | 0.698958 | 00:00 |
| 16 | 3.172076 | 1.136634 | 0.742188 | 00:00 |
| 17 | 2.980874 | 1.020811 | 0.760677 | 00:00 |
| 18 | 2.802851 | 0.934059 | 0.776563 | 00:00 |
| 19 | 2.638977 | 0.868563 | 0.793750 | 00:00 |
| 20 | 2.488060 | 0.817468 | 0.802865 | 00:00 |
| 21 | 2.350771 | 0.782762 | 0.811979 | 00:00 |
| 22 | 2.226339 | 0.768921 | 0.813802 | 00:00 |
| 23 | 2.110989 | 0.753730 | 0.814323 | 00:00 |
| 24 | 2.005932 | 0.741208 | 0.813802 | 00:00 |
| 25 | 1.910660 | 0.737915 | 0.814323 | 00:00 |
| 26 | 1.823836 | 0.729591 | 0.814323 | 00:00 |
| 27 | 1.744348 | 0.727745 | 0.816146 | 00:00 |
| 28 | 1.672214 | 0.726062 | 0.815104 | 00:00 |
| 29 | 1.604780 | 0.718714 | 0.814583 | 00:00 |
| 30 | 1.542345 | 0.718500 | 0.815365 | 00:00 |
| 31 | 1.484714 | 0.715432 | 0.815625 | 00:00 |
| 32 | 1.431729 | 0.717795 | 0.814063 | 00:00 |
| 33 | 1.383804 | 0.715671 | 0.815365 | 00:00 |
| 34 | 1.338941 | 0.714684 | 0.814063 | 00:00 |
| 35 | 1.296706 | 0.713797 | 0.814063 | 00:00 |
| 36 | 1.258308 | 0.713506 | 0.815365 | 00:00 |
| 37 | 1.222434 | 0.713756 | 0.815104 | 00:00 |
| 38 | 1.189285 | 0.713101 | 0.814583 | 00:00 |
| 39 | 1.158855 | 0.714123 | 0.815104 | 00:00 |
| 40 | 1.130809 | 0.713216 | 0.815104 | 00:00 |
| 41 | 1.103926 | 0.711621 | 0.815365 | 00:00 |
| 42 | 1.079272 | 0.711747 | 0.815365 | 00:00 |
| 43 | 1.056297 | 0.711242 | 0.815365 | 00:00 |
| 44 | 1.033900 | 0.711491 | 0.814323 | 00:00 |
| 45 | 1.013398 | 0.710513 | 0.815625 | 00:00 |
| 46 | 0.994760 | 0.710149 | 0.815365 | 00:00 |
| 47 | 0.977171 | 0.711718 | 0.814583 | 00:00 |
| 48 | 0.960160 | 0.710829 | 0.813802 | 00:00 |
| 49 | 0.945268 | 0.710125 | 0.814844 | 00:00 |
| 50 | 0.930707 | 0.712111 | 0.814063 | 00:00 |
Paths: 318 , Tokens: 150 [8, 56, 79, 127] 0 Adj. Rand Index Score: 0.543752. Adj. Mutual Info Score: 0.542515. Classes: [0. 1.] n_clusters: 2.0 Seed: 1133557799
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 5.087507 | 5.045917 | 0.004948 | 00:00 |
| 2 | 5.062593 | 5.035278 | 0.009635 | 00:00 |
| 3 | 5.029911 | 5.015891 | 0.013281 | 00:00 |
| 4 | 4.990533 | 4.981647 | 0.049219 | 00:00 |
| 5 | 4.936895 | 4.925339 | 0.078385 | 00:00 |
| 6 | 4.870072 | 4.835527 | 0.139323 | 00:00 |
| 7 | 4.789662 | 4.698284 | 0.261458 | 00:00 |
| 8 | 4.693760 | 4.488612 | 0.394792 | 00:00 |
| 9 | 4.578603 | 4.170987 | 0.498177 | 00:00 |
| 10 | 4.439440 | 3.701710 | 0.571615 | 00:00 |
| 11 | 4.275474 | 3.074589 | 0.606510 | 00:00 |
| 12 | 4.086166 | 2.435566 | 0.618490 | 00:00 |
| 13 | 3.876294 | 1.919376 | 0.653125 | 00:00 |
| 14 | 3.658365 | 1.575526 | 0.678906 | 00:00 |
| 15 | 3.444104 | 1.331026 | 0.704167 | 00:00 |
| 16 | 3.236078 | 1.157221 | 0.741406 | 00:00 |
| 17 | 3.041204 | 1.036469 | 0.759115 | 00:00 |
| 18 | 2.861330 | 0.940342 | 0.781250 | 00:00 |
| 19 | 2.695106 | 0.874925 | 0.793229 | 00:00 |
| 20 | 2.541441 | 0.828539 | 0.793229 | 00:00 |
| 21 | 2.402561 | 0.794235 | 0.810156 | 00:00 |
| 22 | 2.273550 | 0.768929 | 0.811458 | 00:00 |
| 23 | 2.155581 | 0.762280 | 0.803125 | 00:00 |
| 24 | 2.049151 | 0.747793 | 0.812240 | 00:00 |
| 25 | 1.949112 | 0.741491 | 0.813802 | 00:00 |
| 26 | 1.859360 | 0.733996 | 0.813542 | 00:00 |
| 27 | 1.777787 | 0.728658 | 0.815104 | 00:00 |
| 28 | 1.701930 | 0.729377 | 0.814583 | 00:00 |
| 29 | 1.631600 | 0.725585 | 0.814063 | 00:00 |
| 30 | 1.567907 | 0.721479 | 0.814063 | 00:00 |
| 31 | 1.509243 | 0.720281 | 0.814583 | 00:00 |
| 32 | 1.454885 | 0.721088 | 0.813802 | 00:00 |
| 33 | 1.405090 | 0.720450 | 0.814323 | 00:00 |
| 34 | 1.358923 | 0.716172 | 0.815104 | 00:00 |
| 35 | 1.316465 | 0.716573 | 0.815365 | 00:00 |
| 36 | 1.276987 | 0.716664 | 0.813802 | 00:00 |
| 37 | 1.240786 | 0.714286 | 0.814323 | 00:00 |
| 38 | 1.206641 | 0.714445 | 0.813802 | 00:00 |
| 39 | 1.175316 | 0.713684 | 0.814323 | 00:00 |
| 40 | 1.145060 | 0.713118 | 0.814583 | 00:00 |
| 41 | 1.117517 | 0.710906 | 0.816927 | 00:00 |
| 42 | 1.090879 | 0.713578 | 0.814323 | 00:00 |
| 43 | 1.066236 | 0.712237 | 0.814583 | 00:00 |
| 44 | 1.043818 | 0.711371 | 0.815365 | 00:00 |
| 45 | 1.023188 | 0.710751 | 0.815365 | 00:00 |
| 46 | 1.004117 | 0.711486 | 0.814323 | 00:00 |
| 47 | 0.985909 | 0.711122 | 0.813802 | 00:00 |
| 48 | 0.968562 | 0.710343 | 0.815885 | 00:00 |
| 49 | 0.952481 | 0.711430 | 0.815365 | 00:00 |
| 50 | 0.938257 | 0.711884 | 0.814844 | 00:00 |
Paths: 318 , Tokens: 150 [8, 56, 79, 127] 0 Adj. Rand Index Score: 0.543752. Adj. Mutual Info Score: 0.542515. Classes: [0. 1.] n_clusters: 2.0 Seed: 22446688
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 5.084162 | 4.978789 | 0.002083 | 00:00 |
| 2 | 5.061502 | 4.972279 | 0.001302 | 00:00 |
| 3 | 5.033481 | 4.957591 | 0.009896 | 00:00 |
| 4 | 4.991902 | 4.930260 | 0.039583 | 00:00 |
| 5 | 4.938540 | 4.881417 | 0.144792 | 00:00 |
| 6 | 4.874043 | 4.800850 | 0.217448 | 00:00 |
| 7 | 4.796447 | 4.670933 | 0.311979 | 00:00 |
| 8 | 4.706205 | 4.469481 | 0.390625 | 00:00 |
| 9 | 4.598732 | 4.152863 | 0.461198 | 00:00 |
| 10 | 4.471431 | 3.690967 | 0.541146 | 00:00 |
| 11 | 4.320179 | 3.121761 | 0.610677 | 00:00 |
| 12 | 4.142712 | 2.533847 | 0.643490 | 00:00 |
| 13 | 3.942793 | 2.017585 | 0.662500 | 00:00 |
| 14 | 3.731364 | 1.650611 | 0.682031 | 00:00 |
| 15 | 3.517501 | 1.417474 | 0.707552 | 00:00 |
| 16 | 3.311614 | 1.233590 | 0.723958 | 00:00 |
| 17 | 3.118135 | 1.104095 | 0.745833 | 00:00 |
| 18 | 2.936595 | 0.998106 | 0.764583 | 00:00 |
| 19 | 2.768571 | 0.923884 | 0.776042 | 00:00 |
| 20 | 2.611956 | 0.868402 | 0.790625 | 00:00 |
| 21 | 2.467072 | 0.822884 | 0.804948 | 00:00 |
| 22 | 2.335667 | 0.791208 | 0.809375 | 00:00 |
| 23 | 2.214746 | 0.773239 | 0.811719 | 00:00 |
| 24 | 2.103855 | 0.756911 | 0.812500 | 00:00 |
| 25 | 2.002645 | 0.749180 | 0.811979 | 00:00 |
| 26 | 1.909560 | 0.739921 | 0.811979 | 00:00 |
| 27 | 1.824234 | 0.734665 | 0.814323 | 00:00 |
| 28 | 1.746520 | 0.728702 | 0.813802 | 00:00 |
| 29 | 1.674873 | 0.725522 | 0.814844 | 00:00 |
| 30 | 1.608321 | 0.722637 | 0.815625 | 00:00 |
| 31 | 1.546618 | 0.719389 | 0.813802 | 00:00 |
| 32 | 1.489218 | 0.719761 | 0.814583 | 00:00 |
| 33 | 1.436940 | 0.718789 | 0.813802 | 00:00 |
| 34 | 1.388551 | 0.718366 | 0.811979 | 00:00 |
| 35 | 1.343403 | 0.715756 | 0.813281 | 00:00 |
| 36 | 1.301745 | 0.714715 | 0.811458 | 00:00 |
| 37 | 1.263082 | 0.714210 | 0.812500 | 00:00 |
| 38 | 1.227539 | 0.714555 | 0.813542 | 00:00 |
| 39 | 1.193965 | 0.712680 | 0.815104 | 00:00 |
| 40 | 1.162813 | 0.711533 | 0.814583 | 00:00 |
| 41 | 1.133311 | 0.713129 | 0.814063 | 00:00 |
| 42 | 1.106414 | 0.711418 | 0.816146 | 00:00 |
| 43 | 1.081423 | 0.711828 | 0.813802 | 00:00 |
| 44 | 1.058762 | 0.712957 | 0.814063 | 00:00 |
| 45 | 1.036988 | 0.712601 | 0.814063 | 00:00 |
| 46 | 1.016708 | 0.713975 | 0.815104 | 00:00 |
| 47 | 0.997987 | 0.711145 | 0.814063 | 00:00 |
| 48 | 0.980055 | 0.712512 | 0.814844 | 00:00 |
| 49 | 0.962953 | 0.712250 | 0.814323 | 00:00 |
| 50 | 0.947530 | 0.711976 | 0.815104 | 00:00 |
Paths: 318 , Tokens: 150 [8, 56, 79, 127] 0 Adj. Rand Index Score: 0.543752. Adj. Mutual Info Score: 0.542515. Classes: [0. 1.] n_clusters: 2.0 Seed: 123456789
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 5.080513 | 5.039024 | 0.000781 | 00:00 |
| 2 | 5.065538 | 5.028779 | 0.001042 | 00:00 |
| 3 | 5.039251 | 5.010608 | 0.008333 | 00:00 |
| 4 | 5.002638 | 4.979975 | 0.027865 | 00:00 |
| 5 | 4.955206 | 4.929317 | 0.055729 | 00:00 |
| 6 | 4.895703 | 4.848574 | 0.178125 | 00:00 |
| 7 | 4.825160 | 4.719636 | 0.247917 | 00:00 |
| 8 | 4.738688 | 4.518165 | 0.367969 | 00:00 |
| 9 | 4.632381 | 4.211682 | 0.456510 | 00:00 |
| 10 | 4.504340 | 3.746900 | 0.508854 | 00:00 |
| 11 | 4.347709 | 3.156683 | 0.578906 | 00:00 |
| 12 | 4.164800 | 2.508336 | 0.595313 | 00:00 |
| 13 | 3.959991 | 1.999943 | 0.642188 | 00:00 |
| 14 | 3.747312 | 1.666628 | 0.668490 | 00:00 |
| 15 | 3.533387 | 1.405532 | 0.697396 | 00:00 |
| 16 | 3.326022 | 1.220334 | 0.729688 | 00:00 |
| 17 | 3.129385 | 1.076680 | 0.756250 | 00:00 |
| 18 | 2.943322 | 0.969913 | 0.776563 | 00:00 |
| 19 | 2.772202 | 0.890065 | 0.793229 | 00:00 |
| 20 | 2.612848 | 0.838147 | 0.801823 | 00:00 |
| 21 | 2.466713 | 0.801093 | 0.807552 | 00:00 |
| 22 | 2.332352 | 0.784425 | 0.808333 | 00:00 |
| 23 | 2.209010 | 0.756983 | 0.812760 | 00:00 |
| 24 | 2.097059 | 0.748420 | 0.814063 | 00:00 |
| 25 | 1.995533 | 0.739219 | 0.814583 | 00:00 |
| 26 | 1.902775 | 0.736002 | 0.813802 | 00:00 |
| 27 | 1.816741 | 0.728255 | 0.813281 | 00:00 |
| 28 | 1.739029 | 0.732671 | 0.813021 | 00:00 |
| 29 | 1.667408 | 0.723062 | 0.814844 | 00:00 |
| 30 | 1.600975 | 0.722526 | 0.814323 | 00:00 |
| 31 | 1.539936 | 0.723015 | 0.814063 | 00:00 |
| 32 | 1.483595 | 0.720027 | 0.813281 | 00:00 |
| 33 | 1.430766 | 0.718962 | 0.813542 | 00:00 |
| 34 | 1.382657 | 0.716166 | 0.814583 | 00:00 |
| 35 | 1.338040 | 0.716900 | 0.813802 | 00:00 |
| 36 | 1.297189 | 0.715151 | 0.814844 | 00:00 |
| 37 | 1.258357 | 0.713379 | 0.815365 | 00:00 |
| 38 | 1.222637 | 0.714045 | 0.814323 | 00:00 |
| 39 | 1.189103 | 0.714204 | 0.814583 | 00:00 |
| 40 | 1.158158 | 0.714074 | 0.814323 | 00:00 |
| 41 | 1.129411 | 0.713255 | 0.815365 | 00:00 |
| 42 | 1.102571 | 0.712319 | 0.814063 | 00:00 |
| 43 | 1.077477 | 0.711571 | 0.815104 | 00:00 |
| 44 | 1.054465 | 0.711778 | 0.815365 | 00:00 |
| 45 | 1.032535 | 0.711855 | 0.814583 | 00:00 |
| 46 | 1.012852 | 0.711828 | 0.814063 | 00:00 |
| 47 | 0.994391 | 0.711362 | 0.814844 | 00:00 |
| 48 | 0.976085 | 0.710760 | 0.814844 | 00:00 |
| 49 | 0.960073 | 0.711206 | 0.815104 | 00:00 |
| 50 | 0.944573 | 0.711677 | 0.814844 | 00:00 |
Paths: 318 , Tokens: 150 [8, 56, 79, 127] Adj. Rand Index Score: 0.543752. Adj. Mutual Info Score: 0.542515. Classes: [0. 1.] n_clusters: 2.0 Seed: 987654321
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 5.051621 | 5.004432 | 0.004687 | 00:00 |
| 2 | 5.023418 | 4.991358 | 0.009635 | 00:00 |
| 3 | 4.985503 | 4.969084 | 0.036458 | 00:00 |
| 4 | 4.934233 | 4.930178 | 0.075260 | 00:00 |
| 5 | 4.867278 | 4.865328 | 0.201302 | 00:00 |
| 6 | 4.784426 | 4.756937 | 0.305208 | 00:00 |
| 7 | 4.684152 | 4.584362 | 0.400521 | 00:00 |
| 8 | 4.563793 | 4.313743 | 0.436198 | 00:00 |
| 9 | 4.423436 | 3.908403 | 0.480990 | 00:00 |
| 10 | 4.259247 | 3.353637 | 0.535938 | 00:00 |
| 11 | 4.073395 | 2.738281 | 0.562760 | 00:00 |
| 12 | 3.870864 | 2.171305 | 0.616927 | 00:00 |
| 13 | 3.662017 | 1.744116 | 0.670833 | 00:00 |
| 14 | 3.451260 | 1.457988 | 0.689844 | 00:00 |
| 15 | 3.247507 | 1.249424 | 0.721615 | 00:00 |
| 16 | 3.054935 | 1.107257 | 0.745833 | 00:00 |
| 17 | 2.873478 | 0.996656 | 0.764844 | 00:00 |
| 18 | 2.705188 | 0.916174 | 0.787500 | 00:00 |
| 19 | 2.548430 | 0.857639 | 0.796354 | 00:00 |
| 20 | 2.405813 | 0.815813 | 0.806250 | 00:00 |
| 21 | 2.274655 | 0.781133 | 0.810156 | 00:00 |
| 22 | 2.155682 | 0.758860 | 0.811979 | 00:00 |
| 23 | 2.046138 | 0.749929 | 0.812760 | 00:00 |
| 24 | 1.945396 | 0.738105 | 0.813802 | 00:00 |
| 25 | 1.854796 | 0.733959 | 0.813542 | 00:00 |
| 26 | 1.771554 | 0.734475 | 0.813802 | 00:00 |
| 27 | 1.695548 | 0.726979 | 0.815104 | 00:00 |
| 28 | 1.624893 | 0.724410 | 0.815104 | 00:00 |
| 29 | 1.560168 | 0.721679 | 0.814323 | 00:00 |
| 30 | 1.501777 | 0.721409 | 0.814063 | 00:00 |
| 31 | 1.447325 | 0.719241 | 0.814063 | 00:00 |
| 32 | 1.397539 | 0.716861 | 0.814583 | 00:00 |
| 33 | 1.351385 | 0.715343 | 0.815625 | 00:00 |
| 34 | 1.308791 | 0.715934 | 0.814323 | 00:00 |
| 35 | 1.269576 | 0.715166 | 0.814063 | 00:00 |
| 36 | 1.233084 | 0.714510 | 0.814323 | 00:00 |
| 37 | 1.198578 | 0.713738 | 0.815104 | 00:00 |
| 38 | 1.166261 | 0.713647 | 0.816146 | 00:00 |
| 39 | 1.137509 | 0.713428 | 0.814323 | 00:00 |
| 40 | 1.110412 | 0.712968 | 0.814844 | 00:00 |
| 41 | 1.084492 | 0.713432 | 0.814323 | 00:00 |
| 42 | 1.060961 | 0.712452 | 0.814323 | 00:00 |
| 43 | 1.038633 | 0.710973 | 0.815104 | 00:00 |
| 44 | 1.017713 | 0.711481 | 0.816667 | 00:00 |
| 45 | 0.998368 | 0.711188 | 0.816146 | 00:00 |
| 46 | 0.980284 | 0.712332 | 0.815885 | 00:00 |
| 47 | 0.964079 | 0.711588 | 0.814583 | 00:00 |
| 48 | 0.947888 | 0.709864 | 0.816146 | 00:00 |
| 49 | 0.932831 | 0.711388 | 0.813542 | 00:00 |
| 50 | 0.919292 | 0.710636 | 0.815104 | 00:00 |
Paths: 318 , Tokens: 150 [8, 56, 79, 127] 0 Adj. Rand Index Score: 0.543752. Adj. Mutual Info Score: 0.542515. Classes: [0. 1.] n_clusters: 2.0 Seed: 86420
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 5.062465 | 5.034970 | 0.002604 | 00:00 |
| 2 | 5.041939 | 5.024042 | 0.002344 | 00:00 |
| 3 | 5.009276 | 5.005023 | 0.003385 | 00:00 |
| 4 | 4.967320 | 4.971904 | 0.018229 | 00:00 |
| 5 | 4.912605 | 4.916266 | 0.066146 | 00:00 |
| 6 | 4.841670 | 4.824973 | 0.220833 | 00:00 |
| 7 | 4.756890 | 4.676383 | 0.304688 | 00:00 |
| 8 | 4.655982 | 4.437275 | 0.386198 | 00:00 |
| 9 | 4.534377 | 4.064283 | 0.423698 | 00:00 |
| 10 | 4.391213 | 3.547959 | 0.513802 | 00:00 |
| 11 | 4.222950 | 2.917741 | 0.563802 | 00:00 |
| 12 | 4.030290 | 2.311091 | 0.606510 | 00:00 |
| 13 | 3.820435 | 1.846185 | 0.650521 | 00:00 |
| 14 | 3.604205 | 1.524963 | 0.686979 | 00:00 |
| 15 | 3.392763 | 1.301613 | 0.716927 | 00:00 |
| 16 | 3.186919 | 1.136265 | 0.753125 | 00:00 |
| 17 | 2.994414 | 1.010122 | 0.766927 | 00:00 |
| 18 | 2.815772 | 0.928209 | 0.780469 | 00:00 |
| 19 | 2.650338 | 0.865135 | 0.797656 | 00:00 |
| 20 | 2.500581 | 0.819734 | 0.801302 | 00:00 |
| 21 | 2.362325 | 0.793434 | 0.807813 | 00:00 |
| 22 | 2.237303 | 0.769273 | 0.810156 | 00:00 |
| 23 | 2.122966 | 0.755138 | 0.812500 | 00:00 |
| 24 | 2.018163 | 0.747379 | 0.811719 | 00:00 |
| 25 | 1.922473 | 0.740426 | 0.812500 | 00:00 |
| 26 | 1.835114 | 0.735446 | 0.812500 | 00:00 |
| 27 | 1.754380 | 0.731640 | 0.814323 | 00:00 |
| 28 | 1.680462 | 0.726926 | 0.813802 | 00:00 |
| 29 | 1.612873 | 0.723810 | 0.814063 | 00:00 |
| 30 | 1.549685 | 0.721998 | 0.814844 | 00:00 |
| 31 | 1.491369 | 0.720166 | 0.815104 | 00:00 |
| 32 | 1.438699 | 0.721309 | 0.813542 | 00:00 |
| 33 | 1.389183 | 0.719433 | 0.813542 | 00:00 |
| 34 | 1.343577 | 0.720069 | 0.814063 | 00:00 |
| 35 | 1.302128 | 0.716098 | 0.814583 | 00:00 |
| 36 | 1.262870 | 0.716497 | 0.815104 | 00:00 |
| 37 | 1.226923 | 0.716386 | 0.813542 | 00:00 |
| 38 | 1.193711 | 0.714971 | 0.813542 | 00:00 |
| 39 | 1.161656 | 0.714142 | 0.813802 | 00:00 |
| 40 | 1.132985 | 0.713628 | 0.815885 | 00:00 |
| 41 | 1.105423 | 0.714364 | 0.813281 | 00:00 |
| 42 | 1.080527 | 0.714796 | 0.815104 | 00:00 |
| 43 | 1.056852 | 0.713363 | 0.814583 | 00:00 |
| 44 | 1.035674 | 0.712588 | 0.815104 | 00:00 |
| 45 | 1.014993 | 0.712605 | 0.814323 | 00:00 |
| 46 | 0.996310 | 0.712647 | 0.814323 | 00:00 |
| 47 | 0.978697 | 0.712741 | 0.813802 | 00:00 |
| 48 | 0.961760 | 0.712424 | 0.814844 | 00:00 |
| 49 | 0.945440 | 0.712350 | 0.813802 | 00:00 |
| 50 | 0.930910 | 0.711798 | 0.813802 | 00:00 |
Paths: 318 , Tokens: 150 [8, 56, 79, 127] 0 Adj. Rand Index Score: 0.543752. Adj. Mutual Info Score: 0.542515. Classes: [0. 1.] n_clusters: 2.0 File Name to Train: data_sets/00_bunch/mnist/mnist.csv ############################################################# HDBSCAN ############################################################# Adj. Rand Index Score: 0.751959. Adj. Mutual Info Score: 0.811806. Classes: [0 1 2 3 4 5 6 7 8] n_clusters: 9 QSHIFTPP ############################################################# Adj. Rand Index Score: 0.815115. Adj. Mutual Info Score: 0.838626. Classes: [ 0 1 2 3 4 5 6 7 8 9 10 11] n_clusters: 12 HQSHIFT ############################################################# Adj. Rand Index Score: 0.751959. Adj. Mutual Info Score: 0.811806. Classes: [0 1 2 3 4 5 6 7 8] n_clusters: 9 Seed: 0
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 7.139834 | 6.856246 | 0.157801 | 00:00 |
| 2 | 6.495890 | 5.880241 | 0.386316 | 00:00 |
| 3 | 5.332153 | 4.197858 | 0.536340 | 00:00 |
| 4 | 3.617595 | 2.325981 | 0.667140 | 00:00 |
| 5 | 2.214389 | 1.379493 | 0.783842 | 00:00 |
| 6 | 1.388684 | 0.919938 | 0.842874 | 00:00 |
| 7 | 0.941080 | 0.681407 | 0.881380 | 00:00 |
| 8 | 0.704067 | 0.563072 | 0.897052 | 00:00 |
| 9 | 0.588664 | 0.520100 | 0.906475 | 00:00 |
| 10 | 0.534158 | 0.503652 | 0.905374 | 00:00 |
| 11 | 0.512362 | 0.505147 | 0.909470 | 00:00 |
| 12 | 0.502439 | 0.498852 | 0.909387 | 00:00 |
| 13 | 0.498642 | 0.497989 | 0.903516 | 00:00 |
| 14 | 0.494438 | 0.489860 | 0.909541 | 00:00 |
| 15 | 0.489800 | 0.490640 | 0.908771 | 00:00 |
| 16 | 0.492037 | 0.494470 | 0.907315 | 00:00 |
| 17 | 0.490121 | 0.486527 | 0.908180 | 00:00 |
| 18 | 0.490851 | 0.488768 | 0.909659 | 00:00 |
| 19 | 0.488004 | 0.484760 | 0.908499 | 00:00 |
| 20 | 0.485410 | 0.485034 | 0.909659 | 00:00 |
| 21 | 0.484778 | 0.483784 | 0.909612 | 00:00 |
| 22 | 0.481594 | 0.483889 | 0.905682 | 00:00 |
| 23 | 0.482933 | 0.485561 | 0.909612 | 00:00 |
| 24 | 0.481511 | 0.479609 | 0.909612 | 00:00 |
| 25 | 0.480964 | 0.482648 | 0.909564 | 00:00 |
| 26 | 0.479145 | 0.481213 | 0.909576 | 00:00 |
| 27 | 0.479743 | 0.481316 | 0.905895 | 00:00 |
| 28 | 0.478331 | 0.480465 | 0.909612 | 00:00 |
| 29 | 0.479267 | 0.478104 | 0.909612 | 00:00 |
| 30 | 0.480463 | 0.480718 | 0.909588 | 00:00 |
| 31 | 0.479758 | 0.478105 | 0.909564 | 00:00 |
| 32 | 0.477829 | 0.476765 | 0.909659 | 00:00 |
| 33 | 0.477735 | 0.476978 | 0.909612 | 00:00 |
| 34 | 0.476296 | 0.478712 | 0.909600 | 00:00 |
| 35 | 0.476776 | 0.477263 | 0.907517 | 00:00 |
| 36 | 0.476390 | 0.475721 | 0.909647 | 00:00 |
| 37 | 0.474642 | 0.475749 | 0.909600 | 00:00 |
| 38 | 0.475441 | 0.474796 | 0.909588 | 00:00 |
| 39 | 0.473267 | 0.474929 | 0.909600 | 00:00 |
| 40 | 0.473675 | 0.474736 | 0.909612 | 00:00 |
| 41 | 0.474492 | 0.474019 | 0.909612 | 00:00 |
| 42 | 0.472443 | 0.474091 | 0.909588 | 00:00 |
| 43 | 0.472998 | 0.474064 | 0.909600 | 00:00 |
| 44 | 0.470832 | 0.473647 | 0.909647 | 00:00 |
| 45 | 0.473936 | 0.473568 | 0.909612 | 00:00 |
| 46 | 0.472841 | 0.473556 | 0.909588 | 00:00 |
| 47 | 0.470716 | 0.473463 | 0.909635 | 00:00 |
| 48 | 0.473756 | 0.473491 | 0.909588 | 00:00 |
| 49 | 0.473536 | 0.473477 | 0.909624 | 00:00 |
| 50 | 0.472249 | 0.473510 | 0.909647 | 00:00 |
/home/can/anaconda3/envs/hqshift/lib/python3.6/site-packages/fastai/datasets.py:153: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details. with open(fpath, 'r') as yaml_file: return yaml.load(yaml_file)
Paths: 4685 , Tokens: 1797 [167, 352, 1076, 1457, 1505, 1670, 1741, 1762, 1764] 0 0 Adj. Rand Index Score: 0.770185. Adj. Mutual Info Score: 0.826536. Classes: [0. 1. 2. 3. 4. 5. 6. 7. 8.] n_clusters: 9.0 Seed: 23
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 7.262473 | 7.039093 | 0.118963 | 00:00 |
| 2 | 6.720097 | 6.198088 | 0.369957 | 00:00 |
| 3 | 5.655553 | 4.538759 | 0.550225 | 00:00 |
| 4 | 3.883855 | 2.475403 | 0.648923 | 00:00 |
| 5 | 2.363581 | 1.459030 | 0.773745 | 00:00 |
| 6 | 1.456707 | 0.951614 | 0.839145 | 00:00 |
| 7 | 0.969005 | 0.686519 | 0.882351 | 00:00 |
| 8 | 0.714755 | 0.574102 | 0.896058 | 00:00 |
| 9 | 0.591427 | 0.526857 | 0.900095 | 00:00 |
| 10 | 0.532128 | 0.512261 | 0.907008 | 00:00 |
| 11 | 0.510479 | 0.498501 | 0.909399 | 00:00 |
| 12 | 0.500510 | 0.498322 | 0.905563 | 00:00 |
| 13 | 0.497355 | 0.488452 | 0.909470 | 00:00 |
| 14 | 0.496399 | 0.497573 | 0.906972 | 00:00 |
| 15 | 0.493976 | 0.490543 | 0.909576 | 00:00 |
| 16 | 0.489823 | 0.486393 | 0.908819 | 00:00 |
| 17 | 0.490957 | 0.491240 | 0.906984 | 00:00 |
| 18 | 0.487458 | 0.483669 | 0.909576 | 00:00 |
| 19 | 0.485854 | 0.487333 | 0.909588 | 00:00 |
| 20 | 0.486640 | 0.486980 | 0.908925 | 00:00 |
| 21 | 0.484153 | 0.482609 | 0.909505 | 00:00 |
| 22 | 0.483531 | 0.478937 | 0.908404 | 00:00 |
| 23 | 0.482336 | 0.481828 | 0.909624 | 00:00 |
| 24 | 0.481330 | 0.479362 | 0.909612 | 00:00 |
| 25 | 0.478200 | 0.478646 | 0.909600 | 00:00 |
| 26 | 0.478573 | 0.482239 | 0.909612 | 00:00 |
| 27 | 0.478610 | 0.478790 | 0.909624 | 00:00 |
| 28 | 0.478344 | 0.478154 | 0.909576 | 00:00 |
| 29 | 0.477875 | 0.479084 | 0.909600 | 00:00 |
| 30 | 0.477203 | 0.477124 | 0.909564 | 00:00 |
| 31 | 0.478529 | 0.478250 | 0.909624 | 00:00 |
| 32 | 0.477337 | 0.477212 | 0.909588 | 00:00 |
| 33 | 0.475274 | 0.476131 | 0.909553 | 00:00 |
| 34 | 0.474690 | 0.475484 | 0.909612 | 00:00 |
| 35 | 0.476066 | 0.476205 | 0.909576 | 00:00 |
| 36 | 0.474963 | 0.475565 | 0.909588 | 00:00 |
| 37 | 0.474402 | 0.475218 | 0.909600 | 00:00 |
| 38 | 0.474527 | 0.474698 | 0.909600 | 00:00 |
| 39 | 0.473419 | 0.475215 | 0.909553 | 00:00 |
| 40 | 0.474944 | 0.474303 | 0.909612 | 00:00 |
| 41 | 0.474215 | 0.474101 | 0.909600 | 00:00 |
| 42 | 0.476482 | 0.473975 | 0.909612 | 00:00 |
| 43 | 0.474626 | 0.473904 | 0.909612 | 00:00 |
| 44 | 0.473840 | 0.473713 | 0.909612 | 00:00 |
| 45 | 0.473789 | 0.473644 | 0.909588 | 00:00 |
| 46 | 0.474323 | 0.473460 | 0.909612 | 00:00 |
| 47 | 0.471834 | 0.473436 | 0.909576 | 00:00 |
| 48 | 0.471923 | 0.473423 | 0.909553 | 00:00 |
| 49 | 0.472616 | 0.473414 | 0.909600 | 00:00 |
| 50 | 0.471968 | 0.473415 | 0.909588 | 00:00 |
Paths: 4685 , Tokens: 1797 [167, 352, 1076, 1457, 1505, 1670, 1741, 1762, 1764] 0 Adj. Rand Index Score: 0.768024. Adj. Mutual Info Score: 0.823528. Classes: [0. 1. 2. 3. 4. 5. 6. 7. 8.] n_clusters: 9.0 Seed: 42
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 7.237183 | 7.008329 | 0.092448 | 00:00 |
| 2 | 6.689783 | 6.174602 | 0.318987 | 00:00 |
| 3 | 5.623013 | 4.502342 | 0.511174 | 00:00 |
| 4 | 3.887360 | 2.514932 | 0.645147 | 00:00 |
| 5 | 2.375195 | 1.462777 | 0.770691 | 00:00 |
| 6 | 1.462708 | 0.941634 | 0.841170 | 00:00 |
| 7 | 0.968798 | 0.694985 | 0.879545 | 00:00 |
| 8 | 0.714658 | 0.563596 | 0.901184 | 00:00 |
| 9 | 0.589071 | 0.524884 | 0.906984 | 00:00 |
| 10 | 0.532819 | 0.505758 | 0.906830 | 00:00 |
| 11 | 0.510661 | 0.505767 | 0.902794 | 00:00 |
| 12 | 0.502772 | 0.500097 | 0.904652 | 00:00 |
| 13 | 0.497606 | 0.498057 | 0.909564 | 00:00 |
| 14 | 0.495466 | 0.491697 | 0.908345 | 00:00 |
| 15 | 0.492932 | 0.490789 | 0.907860 | 00:00 |
| 16 | 0.493489 | 0.495939 | 0.905575 | 00:00 |
| 17 | 0.490989 | 0.489260 | 0.909576 | 00:00 |
| 18 | 0.490066 | 0.489171 | 0.909588 | 00:00 |
| 19 | 0.487431 | 0.486174 | 0.909541 | 00:00 |
| 20 | 0.487017 | 0.488200 | 0.909576 | 00:00 |
| 21 | 0.486993 | 0.486831 | 0.909541 | 00:00 |
| 22 | 0.485170 | 0.489099 | 0.905646 | 00:00 |
| 23 | 0.483590 | 0.484716 | 0.909565 | 00:00 |
| 24 | 0.482284 | 0.484157 | 0.908688 | 00:00 |
| 25 | 0.482149 | 0.480686 | 0.909612 | 00:00 |
| 26 | 0.482347 | 0.479204 | 0.909635 | 00:00 |
| 27 | 0.480474 | 0.478702 | 0.909565 | 00:00 |
| 28 | 0.480784 | 0.480063 | 0.909612 | 00:00 |
| 29 | 0.479200 | 0.478441 | 0.909564 | 00:00 |
| 30 | 0.479817 | 0.479259 | 0.909588 | 00:00 |
| 31 | 0.477221 | 0.478664 | 0.907031 | 00:00 |
| 32 | 0.477068 | 0.477983 | 0.909553 | 00:00 |
| 33 | 0.476993 | 0.477829 | 0.907315 | 00:00 |
| 34 | 0.479856 | 0.478425 | 0.909505 | 00:00 |
| 35 | 0.477660 | 0.476007 | 0.909564 | 00:00 |
| 36 | 0.478040 | 0.475963 | 0.909647 | 00:00 |
| 37 | 0.476770 | 0.477255 | 0.909612 | 00:00 |
| 38 | 0.478243 | 0.475564 | 0.909635 | 00:00 |
| 39 | 0.475494 | 0.474692 | 0.909659 | 00:00 |
| 40 | 0.475458 | 0.474561 | 0.909576 | 00:00 |
| 41 | 0.476398 | 0.474634 | 0.909635 | 00:00 |
| 42 | 0.473488 | 0.474357 | 0.909600 | 00:00 |
| 43 | 0.473692 | 0.474145 | 0.909576 | 00:00 |
| 44 | 0.472766 | 0.474005 | 0.909600 | 00:00 |
| 45 | 0.473932 | 0.473798 | 0.909600 | 00:00 |
| 46 | 0.472268 | 0.473732 | 0.909647 | 00:00 |
| 47 | 0.472926 | 0.473672 | 0.909671 | 00:00 |
| 48 | 0.474104 | 0.473675 | 0.909624 | 00:00 |
| 49 | 0.473660 | 0.473656 | 0.909624 | 00:00 |
| 50 | 0.473433 | 0.473593 | 0.909659 | 00:00 |
Paths: 4685 , Tokens: 1797 [167, 352, 1076, 1457, 1505, 1670, 1741, 1762, 1764] Adj. Rand Index Score: 0.768024. Adj. Mutual Info Score: 0.823528. Classes: [0. 1. 2. 3. 4. 5. 6. 7. 8.] n_clusters: 9.0 Seed: 1234
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 7.214774 | 6.991594 | 0.128492 | 00:00 |
| 2 | 6.655304 | 6.115685 | 0.380149 | 00:00 |
| 3 | 5.582345 | 4.473593 | 0.551219 | 00:00 |
| 4 | 3.837204 | 2.467131 | 0.655599 | 00:00 |
| 5 | 2.332497 | 1.428497 | 0.779711 | 00:00 |
| 6 | 1.438917 | 0.935282 | 0.843703 | 00:00 |
| 7 | 0.958817 | 0.681576 | 0.882706 | 00:00 |
| 8 | 0.707217 | 0.564512 | 0.899858 | 00:00 |
| 9 | 0.586759 | 0.523660 | 0.907209 | 00:00 |
| 10 | 0.531431 | 0.505467 | 0.906877 | 00:00 |
| 11 | 0.508525 | 0.496894 | 0.909387 | 00:00 |
| 12 | 0.498237 | 0.492717 | 0.908487 | 00:00 |
| 13 | 0.497463 | 0.492242 | 0.906794 | 00:00 |
| 14 | 0.494345 | 0.494157 | 0.907730 | 00:00 |
| 15 | 0.491445 | 0.488916 | 0.909517 | 00:00 |
| 16 | 0.488471 | 0.491487 | 0.909162 | 00:00 |
| 17 | 0.487839 | 0.489087 | 0.909198 | 00:00 |
| 18 | 0.489186 | 0.488720 | 0.906700 | 00:00 |
| 19 | 0.487020 | 0.482693 | 0.909553 | 00:00 |
| 20 | 0.484953 | 0.481565 | 0.909564 | 00:00 |
| 21 | 0.484067 | 0.487397 | 0.909600 | 00:00 |
| 22 | 0.481917 | 0.480557 | 0.909635 | 00:00 |
| 23 | 0.481267 | 0.481233 | 0.909541 | 00:00 |
| 24 | 0.483359 | 0.480320 | 0.909576 | 00:00 |
| 25 | 0.480955 | 0.479775 | 0.909576 | 00:00 |
| 26 | 0.479888 | 0.480651 | 0.909564 | 00:00 |
| 27 | 0.480122 | 0.477693 | 0.908594 | 00:00 |
| 28 | 0.478762 | 0.477567 | 0.909564 | 00:00 |
| 29 | 0.479893 | 0.479124 | 0.909588 | 00:00 |
| 30 | 0.478145 | 0.476815 | 0.909588 | 00:00 |
| 31 | 0.478366 | 0.478124 | 0.909588 | 00:00 |
| 32 | 0.477058 | 0.477589 | 0.909541 | 00:00 |
| 33 | 0.476952 | 0.476330 | 0.909612 | 00:00 |
| 34 | 0.475457 | 0.476516 | 0.909635 | 00:00 |
| 35 | 0.474447 | 0.477025 | 0.909635 | 00:00 |
| 36 | 0.475257 | 0.475721 | 0.909600 | 00:00 |
| 37 | 0.477449 | 0.474758 | 0.909588 | 00:00 |
| 38 | 0.477072 | 0.475013 | 0.909612 | 00:00 |
| 39 | 0.475678 | 0.475180 | 0.909612 | 00:00 |
| 40 | 0.475252 | 0.474115 | 0.909612 | 00:00 |
| 41 | 0.473829 | 0.474066 | 0.909541 | 00:00 |
| 42 | 0.474177 | 0.473926 | 0.909600 | 00:00 |
| 43 | 0.474295 | 0.474014 | 0.909600 | 00:00 |
| 44 | 0.474327 | 0.473713 | 0.909612 | 00:00 |
| 45 | 0.475060 | 0.473690 | 0.909612 | 00:00 |
| 46 | 0.473067 | 0.473501 | 0.909600 | 00:00 |
| 47 | 0.472677 | 0.473487 | 0.909576 | 00:00 |
| 48 | 0.471514 | 0.473457 | 0.909659 | 00:00 |
| 49 | 0.471633 | 0.473451 | 0.909635 | 00:00 |
| 50 | 0.471660 | 0.473440 | 0.909612 | 00:00 |
Paths: 4685 , Tokens: 1797 [167, 352, 1076, 1457, 1505, 1670, 1741, 1762, 1764] 0 Adj. Rand Index Score: 0.768024. Adj. Mutual Info Score: 0.823528. Classes: [0. 1. 2. 3. 4. 5. 6. 7. 8.] n_clusters: 9.0 Seed: 43210
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 7.265277 | 7.073403 | 0.115376 | 00:00 |
| 2 | 6.791418 | 6.322806 | 0.367318 | 00:00 |
| 3 | 5.815885 | 4.777507 | 0.525379 | 00:00 |
| 4 | 4.082221 | 2.651611 | 0.628847 | 00:00 |
| 5 | 2.500208 | 1.528695 | 0.766181 | 00:00 |
| 6 | 1.523875 | 0.959164 | 0.840803 | 00:00 |
| 7 | 0.993554 | 0.699991 | 0.877261 | 00:00 |
| 8 | 0.723751 | 0.568089 | 0.899254 | 00:00 |
| 9 | 0.592937 | 0.528485 | 0.903634 | 00:00 |
| 10 | 0.537050 | 0.504412 | 0.904865 | 00:00 |
| 11 | 0.512134 | 0.501395 | 0.909434 | 00:00 |
| 12 | 0.503093 | 0.500311 | 0.907599 | 00:00 |
| 13 | 0.497467 | 0.490093 | 0.907149 | 00:00 |
| 14 | 0.495976 | 0.493686 | 0.905398 | 00:00 |
| 15 | 0.492793 | 0.489005 | 0.906889 | 00:00 |
| 16 | 0.489818 | 0.488660 | 0.909564 | 00:00 |
| 17 | 0.490158 | 0.487568 | 0.909553 | 00:00 |
| 18 | 0.488628 | 0.485087 | 0.909233 | 00:00 |
| 19 | 0.488076 | 0.487984 | 0.906913 | 00:00 |
| 20 | 0.486229 | 0.485579 | 0.909529 | 00:00 |
| 21 | 0.488159 | 0.485597 | 0.907955 | 00:00 |
| 22 | 0.484318 | 0.482837 | 0.909588 | 00:00 |
| 23 | 0.482363 | 0.482621 | 0.909245 | 00:00 |
| 24 | 0.482733 | 0.482883 | 0.909564 | 00:00 |
| 25 | 0.481931 | 0.478743 | 0.909541 | 00:00 |
| 26 | 0.482197 | 0.479276 | 0.908546 | 00:00 |
| 27 | 0.481692 | 0.477649 | 0.909576 | 00:00 |
| 28 | 0.480168 | 0.478763 | 0.909541 | 00:00 |
| 29 | 0.479258 | 0.478927 | 0.909564 | 00:00 |
| 30 | 0.478619 | 0.480103 | 0.909576 | 00:00 |
| 31 | 0.477905 | 0.477152 | 0.909576 | 00:00 |
| 32 | 0.476977 | 0.477008 | 0.909600 | 00:00 |
| 33 | 0.479849 | 0.475793 | 0.909635 | 00:00 |
| 34 | 0.477437 | 0.476124 | 0.909624 | 00:00 |
| 35 | 0.477674 | 0.476044 | 0.909588 | 00:00 |
| 36 | 0.474486 | 0.476475 | 0.909588 | 00:00 |
| 37 | 0.476539 | 0.475458 | 0.909564 | 00:00 |
| 38 | 0.475261 | 0.475011 | 0.909600 | 00:00 |
| 39 | 0.475009 | 0.474670 | 0.909600 | 00:00 |
| 40 | 0.473672 | 0.474698 | 0.909588 | 00:00 |
| 41 | 0.475699 | 0.474827 | 0.909600 | 00:00 |
| 42 | 0.475191 | 0.474268 | 0.909647 | 00:00 |
| 43 | 0.472797 | 0.474090 | 0.909600 | 00:00 |
| 44 | 0.473153 | 0.473908 | 0.909600 | 00:00 |
| 45 | 0.473436 | 0.473720 | 0.909635 | 00:00 |
| 46 | 0.470849 | 0.473692 | 0.909636 | 00:00 |
| 47 | 0.472753 | 0.473630 | 0.909564 | 00:00 |
| 48 | 0.471956 | 0.473565 | 0.909600 | 00:00 |
| 49 | 0.474392 | 0.473596 | 0.909612 | 00:00 |
| 50 | 0.472514 | 0.473563 | 0.909612 | 00:00 |
Paths: 4685 , Tokens: 1797 [167, 352, 1076, 1457, 1505, 1670, 1741, 1762, 1764] Adj. Rand Index Score: 0.768024. Adj. Mutual Info Score: 0.823528. Classes: [0. 1. 2. 3. 4. 5. 6. 7. 8.] n_clusters: 9.0 Seed: 1133557799
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 7.158236 | 6.923108 | 0.121970 | 00:00 |
| 2 | 6.596023 | 6.048266 | 0.408535 | 00:00 |
| 3 | 5.502136 | 4.355269 | 0.544425 | 00:00 |
| 4 | 3.753012 | 2.406007 | 0.662725 | 00:00 |
| 5 | 2.290431 | 1.420156 | 0.782907 | 00:00 |
| 6 | 1.420374 | 0.919498 | 0.846543 | 00:00 |
| 7 | 0.947983 | 0.674895 | 0.882505 | 00:00 |
| 8 | 0.700927 | 0.560444 | 0.901882 | 00:00 |
| 9 | 0.582936 | 0.520624 | 0.905114 | 00:00 |
| 10 | 0.532979 | 0.507758 | 0.904001 | 00:00 |
| 11 | 0.507965 | 0.492385 | 0.906866 | 00:00 |
| 12 | 0.499316 | 0.499180 | 0.907481 | 00:00 |
| 13 | 0.496805 | 0.493971 | 0.909541 | 00:00 |
| 14 | 0.494263 | 0.494097 | 0.909008 | 00:00 |
| 15 | 0.492580 | 0.487731 | 0.909186 | 00:00 |
| 16 | 0.491161 | 0.487225 | 0.909553 | 00:00 |
| 17 | 0.489850 | 0.485235 | 0.908452 | 00:00 |
| 18 | 0.486928 | 0.485447 | 0.906108 | 00:00 |
| 19 | 0.486541 | 0.483199 | 0.909588 | 00:00 |
| 20 | 0.486892 | 0.481990 | 0.909588 | 00:00 |
| 21 | 0.483796 | 0.484071 | 0.908369 | 00:00 |
| 22 | 0.482945 | 0.486581 | 0.906416 | 00:00 |
| 23 | 0.481388 | 0.481783 | 0.909576 | 00:00 |
| 24 | 0.482067 | 0.481536 | 0.906984 | 00:00 |
| 25 | 0.479755 | 0.478909 | 0.909612 | 00:00 |
| 26 | 0.480078 | 0.478413 | 0.909576 | 00:00 |
| 27 | 0.478744 | 0.479727 | 0.909576 | 00:00 |
| 28 | 0.478335 | 0.479477 | 0.909553 | 00:00 |
| 29 | 0.478355 | 0.477127 | 0.909588 | 00:00 |
| 30 | 0.477056 | 0.477178 | 0.909541 | 00:00 |
| 31 | 0.477162 | 0.477514 | 0.909576 | 00:00 |
| 32 | 0.475935 | 0.475968 | 0.909588 | 00:00 |
| 33 | 0.477152 | 0.477162 | 0.909588 | 00:00 |
| 34 | 0.476279 | 0.477042 | 0.909576 | 00:00 |
| 35 | 0.477178 | 0.476951 | 0.909624 | 00:00 |
| 36 | 0.475559 | 0.475407 | 0.909553 | 00:00 |
| 37 | 0.474402 | 0.474528 | 0.909612 | 00:00 |
| 38 | 0.475989 | 0.474738 | 0.909576 | 00:00 |
| 39 | 0.475203 | 0.474549 | 0.909600 | 00:00 |
| 40 | 0.474624 | 0.474426 | 0.909624 | 00:00 |
| 41 | 0.475331 | 0.474031 | 0.909564 | 00:00 |
| 42 | 0.473113 | 0.473770 | 0.909612 | 00:00 |
| 43 | 0.473086 | 0.473710 | 0.909600 | 00:00 |
| 44 | 0.473684 | 0.473568 | 0.909624 | 00:00 |
| 45 | 0.472907 | 0.473507 | 0.909600 | 00:00 |
| 46 | 0.472815 | 0.473549 | 0.909565 | 00:00 |
| 47 | 0.471586 | 0.473470 | 0.909612 | 00:00 |
| 48 | 0.472053 | 0.473480 | 0.909612 | 00:00 |
| 49 | 0.472628 | 0.473463 | 0.909600 | 00:00 |
| 50 | 0.473885 | 0.473486 | 0.909600 | 00:00 |
Paths: 4685 , Tokens: 1797 [167, 352, 1076, 1457, 1505, 1670, 1741, 1762, 1764] Adj. Rand Index Score: 0.768024. Adj. Mutual Info Score: 0.823528. Classes: [0. 1. 2. 3. 4. 5. 6. 7. 8.] n_clusters: 9.0 Seed: 22446688
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 7.238947 | 7.043081 | 0.121745 | 00:00 |
| 2 | 6.757493 | 6.259144 | 0.373840 | 00:00 |
| 3 | 5.759178 | 4.690617 | 0.540495 | 00:00 |
| 4 | 4.008903 | 2.599539 | 0.642247 | 00:00 |
| 5 | 2.444153 | 1.507338 | 0.769129 | 00:00 |
| 6 | 1.506878 | 0.978999 | 0.838873 | 00:00 |
| 7 | 0.994948 | 0.692139 | 0.880646 | 00:00 |
| 8 | 0.724774 | 0.571873 | 0.899278 | 00:00 |
| 9 | 0.597714 | 0.521404 | 0.904380 | 00:00 |
| 10 | 0.537247 | 0.504038 | 0.905078 | 00:00 |
| 11 | 0.513395 | 0.501475 | 0.907102 | 00:00 |
| 12 | 0.501942 | 0.491562 | 0.909541 | 00:00 |
| 13 | 0.497007 | 0.499324 | 0.908463 | 00:00 |
| 14 | 0.495277 | 0.493989 | 0.904178 | 00:00 |
| 15 | 0.491108 | 0.493409 | 0.909564 | 00:00 |
| 16 | 0.491773 | 0.489198 | 0.908120 | 00:00 |
| 17 | 0.489184 | 0.483451 | 0.908902 | 00:00 |
| 18 | 0.487346 | 0.491041 | 0.908511 | 00:00 |
| 19 | 0.486141 | 0.483380 | 0.909600 | 00:00 |
| 20 | 0.485188 | 0.482965 | 0.908097 | 00:00 |
| 21 | 0.484326 | 0.493619 | 0.903989 | 00:00 |
| 22 | 0.485536 | 0.488849 | 0.909588 | 00:00 |
| 23 | 0.482960 | 0.482557 | 0.909588 | 00:00 |
| 24 | 0.482638 | 0.479890 | 0.909612 | 00:00 |
| 25 | 0.482563 | 0.487752 | 0.909564 | 00:00 |
| 26 | 0.480852 | 0.479087 | 0.909612 | 00:00 |
| 27 | 0.480019 | 0.481604 | 0.909612 | 00:00 |
| 28 | 0.477972 | 0.479393 | 0.909576 | 00:00 |
| 29 | 0.479797 | 0.481336 | 0.909600 | 00:00 |
| 30 | 0.479706 | 0.477450 | 0.909564 | 00:00 |
| 31 | 0.479115 | 0.477441 | 0.909635 | 00:00 |
| 32 | 0.478287 | 0.477494 | 0.909647 | 00:00 |
| 33 | 0.476412 | 0.478482 | 0.909600 | 00:00 |
| 34 | 0.474765 | 0.477426 | 0.909576 | 00:00 |
| 35 | 0.476755 | 0.476646 | 0.909612 | 00:00 |
| 36 | 0.477862 | 0.475086 | 0.909576 | 00:00 |
| 37 | 0.477082 | 0.475407 | 0.909659 | 00:00 |
| 38 | 0.477076 | 0.475234 | 0.909624 | 00:00 |
| 39 | 0.475062 | 0.475321 | 0.909576 | 00:00 |
| 40 | 0.474797 | 0.474621 | 0.909624 | 00:00 |
| 41 | 0.474567 | 0.474700 | 0.909576 | 00:00 |
| 42 | 0.473946 | 0.474266 | 0.909576 | 00:00 |
| 43 | 0.474285 | 0.474017 | 0.909600 | 00:00 |
| 44 | 0.473647 | 0.473785 | 0.909635 | 00:00 |
| 45 | 0.474183 | 0.473630 | 0.909576 | 00:00 |
| 46 | 0.474191 | 0.473657 | 0.909600 | 00:00 |
| 47 | 0.475801 | 0.473631 | 0.909553 | 00:00 |
| 48 | 0.473713 | 0.473549 | 0.909588 | 00:00 |
| 49 | 0.473379 | 0.473526 | 0.909600 | 00:00 |
| 50 | 0.474855 | 0.473498 | 0.909600 | 00:00 |
Paths: 4685 , Tokens: 1797 [167, 352, 1076, 1457, 1505, 1670, 1741, 1762, 1764] Adj. Rand Index Score: 0.768024. Adj. Mutual Info Score: 0.823528. Classes: [0. 1. 2. 3. 4. 5. 6. 7. 8.] n_clusters: 9.0 Seed: 123456789
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 7.193827 | 6.982358 | 0.107102 | 00:00 |
| 2 | 6.662144 | 6.123871 | 0.394058 | 00:00 |
| 3 | 5.586638 | 4.466891 | 0.533499 | 00:00 |
| 4 | 3.848895 | 2.505474 | 0.638861 | 00:00 |
| 5 | 2.374017 | 1.470520 | 0.770431 | 00:00 |
| 6 | 1.471580 | 0.951046 | 0.843513 | 00:00 |
| 7 | 0.972295 | 0.689432 | 0.874740 | 00:00 |
| 8 | 0.721314 | 0.582920 | 0.891714 | 00:00 |
| 9 | 0.595676 | 0.522418 | 0.905611 | 00:00 |
| 10 | 0.541291 | 0.507318 | 0.902332 | 00:00 |
| 11 | 0.511390 | 0.507058 | 0.905303 | 00:00 |
| 12 | 0.505448 | 0.496534 | 0.909517 | 00:00 |
| 13 | 0.496391 | 0.488733 | 0.909482 | 00:00 |
| 14 | 0.493435 | 0.496408 | 0.909268 | 00:00 |
| 15 | 0.491447 | 0.491266 | 0.909600 | 00:00 |
| 16 | 0.491686 | 0.490357 | 0.908511 | 00:00 |
| 17 | 0.490390 | 0.490522 | 0.909600 | 00:00 |
| 18 | 0.490567 | 0.485864 | 0.907600 | 00:00 |
| 19 | 0.488294 | 0.488276 | 0.908049 | 00:00 |
| 20 | 0.483675 | 0.483098 | 0.909576 | 00:00 |
| 21 | 0.483228 | 0.482630 | 0.909576 | 00:00 |
| 22 | 0.482216 | 0.482832 | 0.909600 | 00:00 |
| 23 | 0.482532 | 0.482412 | 0.908582 | 00:00 |
| 24 | 0.482930 | 0.484539 | 0.909600 | 00:00 |
| 25 | 0.481633 | 0.478996 | 0.909564 | 00:00 |
| 26 | 0.480844 | 0.480753 | 0.909647 | 00:00 |
| 27 | 0.481676 | 0.479475 | 0.909576 | 00:00 |
| 28 | 0.480203 | 0.478419 | 0.909553 | 00:00 |
| 29 | 0.478597 | 0.478354 | 0.909600 | 00:00 |
| 30 | 0.477438 | 0.477923 | 0.909600 | 00:00 |
| 31 | 0.477813 | 0.477213 | 0.909635 | 00:00 |
| 32 | 0.476901 | 0.476789 | 0.909624 | 00:00 |
| 33 | 0.475354 | 0.475937 | 0.909635 | 00:00 |
| 34 | 0.475851 | 0.475580 | 0.909588 | 00:00 |
| 35 | 0.476151 | 0.476213 | 0.909600 | 00:00 |
| 36 | 0.476818 | 0.475552 | 0.909576 | 00:00 |
| 37 | 0.476841 | 0.475861 | 0.909588 | 00:00 |
| 38 | 0.476551 | 0.475532 | 0.909553 | 00:00 |
| 39 | 0.476691 | 0.474675 | 0.909635 | 00:00 |
| 40 | 0.475022 | 0.474213 | 0.909588 | 00:00 |
| 41 | 0.474764 | 0.474242 | 0.909612 | 00:00 |
| 42 | 0.474762 | 0.473871 | 0.909612 | 00:00 |
| 43 | 0.471577 | 0.473844 | 0.909588 | 00:00 |
| 44 | 0.472771 | 0.473752 | 0.909588 | 00:00 |
| 45 | 0.474547 | 0.473595 | 0.909612 | 00:00 |
| 46 | 0.472962 | 0.473533 | 0.909588 | 00:00 |
| 47 | 0.473925 | 0.473503 | 0.909600 | 00:00 |
| 48 | 0.473991 | 0.473528 | 0.909588 | 00:00 |
| 49 | 0.472428 | 0.473534 | 0.909600 | 00:00 |
| 50 | 0.472624 | 0.473466 | 0.909623 | 00:00 |
Paths: 4685 , Tokens: 1797 [167, 352, 1076, 1457, 1505, 1670, 1741, 1762, 1764] Adj. Rand Index Score: 0.768024. Adj. Mutual Info Score: 0.823528. Classes: [0. 1. 2. 3. 4. 5. 6. 7. 8.] n_clusters: 9.0 Seed: 987654321
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 7.243691 | 7.036757 | 0.126148 | 00:00 |
| 2 | 6.737897 | 6.245736 | 0.366915 | 00:00 |
| 3 | 5.731721 | 4.698088 | 0.530564 | 00:00 |
| 4 | 4.001667 | 2.616205 | 0.639737 | 00:00 |
| 5 | 2.449496 | 1.509256 | 0.765566 | 00:00 |
| 6 | 1.506166 | 0.965863 | 0.836813 | 00:00 |
| 7 | 0.988189 | 0.698956 | 0.880350 | 00:00 |
| 8 | 0.723619 | 0.569349 | 0.899882 | 00:00 |
| 9 | 0.593381 | 0.523520 | 0.907162 | 00:00 |
| 10 | 0.539648 | 0.509826 | 0.906629 | 00:00 |
| 11 | 0.513717 | 0.499559 | 0.908902 | 00:00 |
| 12 | 0.503314 | 0.494321 | 0.909469 | 00:00 |
| 13 | 0.496354 | 0.493107 | 0.906960 | 00:00 |
| 14 | 0.496054 | 0.498249 | 0.909576 | 00:00 |
| 15 | 0.493805 | 0.495532 | 0.905990 | 00:00 |
| 16 | 0.490835 | 0.492598 | 0.905931 | 00:00 |
| 17 | 0.490568 | 0.490609 | 0.907008 | 00:00 |
| 18 | 0.490471 | 0.486718 | 0.907114 | 00:00 |
| 19 | 0.489108 | 0.486698 | 0.905421 | 00:00 |
| 20 | 0.488034 | 0.484514 | 0.908819 | 00:00 |
| 21 | 0.486054 | 0.486162 | 0.909541 | 00:00 |
| 22 | 0.483412 | 0.481096 | 0.909541 | 00:00 |
| 23 | 0.485398 | 0.483017 | 0.909588 | 00:00 |
| 24 | 0.482331 | 0.483514 | 0.907055 | 00:00 |
| 25 | 0.480475 | 0.482760 | 0.908854 | 00:00 |
| 26 | 0.481265 | 0.484270 | 0.906593 | 00:00 |
| 27 | 0.479780 | 0.478685 | 0.909564 | 00:00 |
| 28 | 0.479642 | 0.479131 | 0.909564 | 00:00 |
| 29 | 0.478889 | 0.480207 | 0.909635 | 00:00 |
| 30 | 0.481414 | 0.478925 | 0.907528 | 00:00 |
| 31 | 0.480260 | 0.478536 | 0.909600 | 00:00 |
| 32 | 0.478991 | 0.477601 | 0.909576 | 00:00 |
| 33 | 0.479724 | 0.477481 | 0.908866 | 00:00 |
| 34 | 0.478965 | 0.477511 | 0.909588 | 00:00 |
| 35 | 0.476909 | 0.475594 | 0.909576 | 00:00 |
| 36 | 0.476654 | 0.476073 | 0.909612 | 00:00 |
| 37 | 0.476080 | 0.475933 | 0.909588 | 00:00 |
| 38 | 0.475543 | 0.476111 | 0.909588 | 00:00 |
| 39 | 0.476459 | 0.475091 | 0.909576 | 00:00 |
| 40 | 0.474732 | 0.474897 | 0.909600 | 00:00 |
| 41 | 0.473210 | 0.474601 | 0.909600 | 00:00 |
| 42 | 0.473405 | 0.474714 | 0.909588 | 00:00 |
| 43 | 0.472809 | 0.474222 | 0.909612 | 00:00 |
| 44 | 0.472603 | 0.474114 | 0.909576 | 00:00 |
| 45 | 0.472718 | 0.473824 | 0.909600 | 00:00 |
| 46 | 0.475416 | 0.473723 | 0.909600 | 00:00 |
| 47 | 0.474442 | 0.473768 | 0.909576 | 00:00 |
| 48 | 0.472953 | 0.473679 | 0.909635 | 00:00 |
| 49 | 0.473268 | 0.473621 | 0.909647 | 00:00 |
| 50 | 0.470645 | 0.473657 | 0.909647 | 00:00 |
Paths: 4685 , Tokens: 1797 [167, 352, 1076, 1457, 1505, 1670, 1741, 1762, 1764] Adj. Rand Index Score: 0.768024. Adj. Mutual Info Score: 0.823528. Classes: [0. 1. 2. 3. 4. 5. 6. 7. 8.] n_clusters: 9.0 Seed: 86420
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 7.165868 | 6.920221 | 0.129250 | 00:00 |
| 2 | 6.572721 | 5.998789 | 0.332493 | 00:00 |
| 3 | 5.457621 | 4.314236 | 0.529036 | 00:00 |
| 4 | 3.716811 | 2.372469 | 0.655114 | 00:00 |
| 5 | 2.269682 | 1.407940 | 0.776077 | 00:00 |
| 6 | 1.410210 | 0.918973 | 0.847514 | 00:00 |
| 7 | 0.948092 | 0.677954 | 0.881664 | 00:00 |
| 8 | 0.702673 | 0.565478 | 0.901432 | 00:00 |
| 9 | 0.586541 | 0.520813 | 0.903172 | 00:00 |
| 10 | 0.535049 | 0.511576 | 0.900201 | 00:00 |
| 11 | 0.510326 | 0.500359 | 0.908925 | 00:00 |
| 12 | 0.501147 | 0.495555 | 0.909541 | 00:00 |
| 13 | 0.498381 | 0.493842 | 0.909186 | 00:00 |
| 14 | 0.493480 | 0.497370 | 0.909588 | 00:00 |
| 15 | 0.492331 | 0.491710 | 0.909564 | 00:00 |
| 16 | 0.492461 | 0.491894 | 0.904072 | 00:00 |
| 17 | 0.493122 | 0.488029 | 0.908026 | 00:00 |
| 18 | 0.489364 | 0.483508 | 0.908582 | 00:00 |
| 19 | 0.487417 | 0.487976 | 0.909612 | 00:00 |
| 20 | 0.486161 | 0.487269 | 0.907280 | 00:00 |
| 21 | 0.485762 | 0.482517 | 0.909612 | 00:00 |
| 22 | 0.484841 | 0.486907 | 0.909588 | 00:00 |
| 23 | 0.485217 | 0.483928 | 0.909044 | 00:00 |
| 24 | 0.483703 | 0.482598 | 0.907031 | 00:00 |
| 25 | 0.484204 | 0.481293 | 0.909624 | 00:00 |
| 26 | 0.481887 | 0.480456 | 0.909564 | 00:00 |
| 27 | 0.480529 | 0.479673 | 0.909624 | 00:00 |
| 28 | 0.479061 | 0.479270 | 0.909588 | 00:00 |
| 29 | 0.478793 | 0.479016 | 0.909517 | 00:00 |
| 30 | 0.478170 | 0.478963 | 0.909588 | 00:00 |
| 31 | 0.479371 | 0.478957 | 0.909588 | 00:00 |
| 32 | 0.477947 | 0.478725 | 0.909612 | 00:00 |
| 33 | 0.476967 | 0.477571 | 0.909612 | 00:00 |
| 34 | 0.474637 | 0.476869 | 0.909588 | 00:00 |
| 35 | 0.475289 | 0.476540 | 0.909564 | 00:00 |
| 36 | 0.477514 | 0.475982 | 0.909612 | 00:00 |
| 37 | 0.478234 | 0.475372 | 0.909612 | 00:00 |
| 38 | 0.475472 | 0.475616 | 0.909600 | 00:00 |
| 39 | 0.474504 | 0.475134 | 0.909635 | 00:00 |
| 40 | 0.474870 | 0.474629 | 0.909588 | 00:00 |
| 41 | 0.473847 | 0.474575 | 0.909600 | 00:00 |
| 42 | 0.473058 | 0.474222 | 0.909600 | 00:00 |
| 43 | 0.474446 | 0.474082 | 0.909624 | 00:00 |
| 44 | 0.473021 | 0.473810 | 0.909624 | 00:00 |
| 45 | 0.476393 | 0.473723 | 0.909624 | 00:00 |
| 46 | 0.473829 | 0.473646 | 0.909612 | 00:00 |
| 47 | 0.472840 | 0.473683 | 0.909624 | 00:00 |
| 48 | 0.474640 | 0.473603 | 0.909635 | 00:00 |
| 49 | 0.472764 | 0.473607 | 0.909588 | 00:00 |
| 50 | 0.474192 | 0.473587 | 0.909612 | 00:00 |
Paths: 4685 , Tokens: 1797 [167, 352, 1076, 1457, 1505, 1670, 1741, 1762, 1764] Adj. Rand Index Score: 0.768024. Adj. Mutual Info Score: 0.823528. Classes: [0. 1. 2. 3. 4. 5. 6. 7. 8.] n_clusters: 9.0 File Name to Train: data_sets/00_bunch/seeds/seeds.csv ############################################################# HDBSCAN ############################################################# Adj. Rand Index Score: 0.413773. Adj. Mutual Info Score: 0.504953. Classes: [-1 0 1 2] n_clusters: 3 QSHIFTPP ############################################################# Adj. Rand Index Score: 0.752283. Adj. Mutual Info Score: 0.707126. Classes: [0 1 2] n_clusters: 3 HQSHIFT ############################################################# Adj. Rand Index Score: 0.413773. Adj. Mutual Info Score: 0.504953. Classes: [-1 0 1 2] n_clusters: 3 Seed: 0
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 5.286868 | 5.311974 | 0.023438 | 00:00 |
| 2 | 5.250213 | 5.294427 | 0.048633 | 00:00 |
| 3 | 5.205490 | 5.261797 | 0.097266 | 00:00 |
| 4 | 5.144488 | 5.202171 | 0.186133 | 00:00 |
| 5 | 5.066547 | 5.097850 | 0.255664 | 00:00 |
| 6 | 4.967786 | 4.917655 | 0.323047 | 00:00 |
| 7 | 4.846941 | 4.620129 | 0.388086 | 00:00 |
| 8 | 4.696753 | 4.150607 | 0.510742 | 00:00 |
| 9 | 4.513618 | 3.492771 | 0.564844 | 00:00 |
| 10 | 4.292715 | 2.738514 | 0.623242 | 00:00 |
| 11 | 4.040936 | 2.098820 | 0.654297 | 00:00 |
| 12 | 3.775242 | 1.641743 | 0.680469 | 00:00 |
| 13 | 3.506527 | 1.336225 | 0.726953 | 00:00 |
| 14 | 3.250319 | 1.137240 | 0.754297 | 00:00 |
| 15 | 3.012639 | 1.003635 | 0.775195 | 00:00 |
| 16 | 2.793782 | 0.901302 | 0.797070 | 00:00 |
| 17 | 2.595708 | 0.839225 | 0.809375 | 00:00 |
| 18 | 2.414679 | 0.784838 | 0.820117 | 00:00 |
| 19 | 2.252350 | 0.764192 | 0.808789 | 00:00 |
| 20 | 2.107888 | 0.738009 | 0.828125 | 00:00 |
| 21 | 1.977080 | 0.732627 | 0.828711 | 00:00 |
| 22 | 1.860086 | 0.725621 | 0.824023 | 00:00 |
| 23 | 1.754994 | 0.718116 | 0.830469 | 00:00 |
| 24 | 1.660332 | 0.711344 | 0.829688 | 00:00 |
| 25 | 1.574806 | 0.706352 | 0.828906 | 00:00 |
| 26 | 1.497589 | 0.705745 | 0.830078 | 00:00 |
| 27 | 1.428322 | 0.703157 | 0.829102 | 00:00 |
| 28 | 1.365272 | 0.700213 | 0.829102 | 00:00 |
| 29 | 1.308407 | 0.697687 | 0.828906 | 00:00 |
| 30 | 1.256603 | 0.694806 | 0.829883 | 00:00 |
| 31 | 1.208908 | 0.694146 | 0.829297 | 00:00 |
| 32 | 1.165504 | 0.698290 | 0.829687 | 00:00 |
| 33 | 1.127105 | 0.696422 | 0.828320 | 00:00 |
| 34 | 1.089890 | 0.695854 | 0.825195 | 00:00 |
| 35 | 1.057469 | 0.695962 | 0.829102 | 00:00 |
| 36 | 1.027662 | 0.694147 | 0.829687 | 00:00 |
| 37 | 1.000542 | 0.692388 | 0.829297 | 00:00 |
| 38 | 0.975142 | 0.690467 | 0.830273 | 00:00 |
| 39 | 0.952553 | 0.690168 | 0.831445 | 00:00 |
| 40 | 0.930993 | 0.688476 | 0.829883 | 00:00 |
| 41 | 0.911976 | 0.688864 | 0.829102 | 00:00 |
| 42 | 0.893824 | 0.689079 | 0.829688 | 00:00 |
| 43 | 0.878092 | 0.688989 | 0.830078 | 00:00 |
| 44 | 0.862821 | 0.687582 | 0.829492 | 00:00 |
| 45 | 0.848761 | 0.688763 | 0.829297 | 00:00 |
| 46 | 0.836146 | 0.688509 | 0.829687 | 00:00 |
| 47 | 0.823997 | 0.687859 | 0.830469 | 00:00 |
| 48 | 0.812600 | 0.688055 | 0.830859 | 00:00 |
| 49 | 0.803122 | 0.687607 | 0.831250 | 00:00 |
| 50 | 0.794287 | 0.687996 | 0.830469 | 00:00 |
/home/can/anaconda3/envs/hqshift/lib/python3.6/site-packages/fastai/datasets.py:153: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details. with open(fpath, 'r') as yaml_file: return yaml.load(yaml_file)
Paths: 474 , Tokens: 210 [49, 69, 93, 122, 183, 192] 0 Adj. Rand Index Score: 0.403137. Adj. Mutual Info Score: 0.473262. Classes: [0. 1. 2.] n_clusters: 3.0 Seed: 23
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 5.362040 | 5.355941 | 0.017969 | 00:00 |
| 2 | 5.337102 | 5.341494 | 0.018164 | 00:00 |
| 3 | 5.301125 | 5.313323 | 0.021875 | 00:00 |
| 4 | 5.252801 | 5.260508 | 0.063281 | 00:00 |
| 5 | 5.188508 | 5.164976 | 0.169531 | 00:00 |
| 6 | 5.110781 | 4.999864 | 0.281250 | 00:00 |
| 7 | 5.013660 | 4.728194 | 0.410742 | 00:00 |
| 8 | 4.891727 | 4.300388 | 0.488672 | 00:00 |
| 9 | 4.738280 | 3.692496 | 0.534766 | 00:00 |
| 10 | 4.546075 | 2.993240 | 0.567578 | 00:00 |
| 11 | 4.313321 | 2.345271 | 0.597070 | 00:00 |
| 12 | 4.052401 | 1.900291 | 0.626367 | 00:00 |
| 13 | 3.782417 | 1.568115 | 0.677539 | 00:00 |
| 14 | 3.514635 | 1.317998 | 0.711523 | 00:00 |
| 15 | 3.260965 | 1.147087 | 0.743945 | 00:00 |
| 16 | 3.024332 | 0.997360 | 0.770117 | 00:00 |
| 17 | 2.807970 | 0.891327 | 0.788867 | 00:00 |
| 18 | 2.610609 | 0.820602 | 0.814258 | 00:00 |
| 19 | 2.431222 | 0.780308 | 0.822070 | 00:00 |
| 20 | 2.271942 | 0.775019 | 0.818164 | 00:00 |
| 21 | 2.126440 | 0.747827 | 0.820703 | 00:00 |
| 22 | 1.996768 | 0.740493 | 0.812109 | 00:00 |
| 23 | 1.880530 | 0.724981 | 0.829688 | 00:00 |
| 24 | 1.775500 | 0.725853 | 0.823438 | 00:00 |
| 25 | 1.680761 | 0.714503 | 0.829102 | 00:00 |
| 26 | 1.595943 | 0.717820 | 0.824219 | 00:00 |
| 27 | 1.517812 | 0.710794 | 0.828711 | 00:00 |
| 28 | 1.447312 | 0.706404 | 0.829492 | 00:00 |
| 29 | 1.383532 | 0.701807 | 0.830859 | 00:00 |
| 30 | 1.324932 | 0.705167 | 0.830469 | 00:00 |
| 31 | 1.271837 | 0.700173 | 0.828906 | 00:00 |
| 32 | 1.224000 | 0.697861 | 0.829492 | 00:00 |
| 33 | 1.179601 | 0.695018 | 0.830664 | 00:00 |
| 34 | 1.138950 | 0.694716 | 0.829688 | 00:00 |
| 35 | 1.101927 | 0.692798 | 0.831445 | 00:00 |
| 36 | 1.067662 | 0.691137 | 0.829883 | 00:00 |
| 37 | 1.036721 | 0.692137 | 0.830273 | 00:00 |
| 38 | 1.008749 | 0.690808 | 0.829492 | 00:00 |
| 39 | 0.982643 | 0.689343 | 0.830273 | 00:00 |
| 40 | 0.958829 | 0.691090 | 0.830273 | 00:00 |
| 41 | 0.936553 | 0.689414 | 0.830273 | 00:00 |
| 42 | 0.916641 | 0.688294 | 0.829492 | 00:00 |
| 43 | 0.898479 | 0.688981 | 0.829492 | 00:00 |
| 44 | 0.881660 | 0.689489 | 0.830469 | 00:00 |
| 45 | 0.866103 | 0.689593 | 0.829688 | 00:00 |
| 46 | 0.852086 | 0.688925 | 0.829492 | 00:00 |
| 47 | 0.839105 | 0.688756 | 0.829492 | 00:00 |
| 48 | 0.826271 | 0.687900 | 0.830664 | 00:00 |
| 49 | 0.815372 | 0.688304 | 0.830469 | 00:00 |
| 50 | 0.804807 | 0.687329 | 0.830078 | 00:00 |
Paths: 474 , Tokens: 210 [49, 69, 93, 122, 183, 192] 0 Adj. Rand Index Score: 0.403137. Adj. Mutual Info Score: 0.473262. Classes: [0. 1. 2.] n_clusters: 3.0 Seed: 42
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 5.363315 | 5.339903 | 0.002539 | 00:00 |
| 2 | 5.337633 | 5.325966 | 0.003320 | 00:00 |
| 3 | 5.303735 | 5.300277 | 0.008008 | 00:00 |
| 4 | 5.258486 | 5.252803 | 0.058594 | 00:00 |
| 5 | 5.200274 | 5.168670 | 0.176953 | 00:00 |
| 6 | 5.128747 | 5.027593 | 0.232422 | 00:00 |
| 7 | 5.038327 | 4.800417 | 0.355078 | 00:00 |
| 8 | 4.924458 | 4.446014 | 0.411719 | 00:00 |
| 9 | 4.779312 | 3.918836 | 0.474023 | 00:00 |
| 10 | 4.596964 | 3.206694 | 0.549023 | 00:00 |
| 11 | 4.370150 | 2.461984 | 0.600391 | 00:00 |
| 12 | 4.111202 | 1.899392 | 0.640625 | 00:00 |
| 13 | 3.839419 | 1.528348 | 0.700000 | 00:00 |
| 14 | 3.570930 | 1.269776 | 0.739648 | 00:00 |
| 15 | 3.314037 | 1.085884 | 0.765039 | 00:00 |
| 16 | 3.073373 | 0.965271 | 0.783203 | 00:00 |
| 17 | 2.852495 | 0.881212 | 0.803320 | 00:00 |
| 18 | 2.651106 | 0.827014 | 0.812891 | 00:00 |
| 19 | 2.468402 | 0.775089 | 0.822461 | 00:00 |
| 20 | 2.304673 | 0.754952 | 0.825391 | 00:00 |
| 21 | 2.155844 | 0.738223 | 0.827930 | 00:00 |
| 22 | 2.022914 | 0.731568 | 0.827734 | 00:00 |
| 23 | 1.903251 | 0.721757 | 0.827344 | 00:00 |
| 24 | 1.796245 | 0.714243 | 0.830469 | 00:00 |
| 25 | 1.700152 | 0.712697 | 0.828516 | 00:00 |
| 26 | 1.613064 | 0.706485 | 0.829687 | 00:00 |
| 27 | 1.533670 | 0.700547 | 0.826758 | 00:00 |
| 28 | 1.462123 | 0.701916 | 0.830664 | 00:00 |
| 29 | 1.396573 | 0.697537 | 0.828906 | 00:00 |
| 30 | 1.337570 | 0.698121 | 0.829492 | 00:00 |
| 31 | 1.283258 | 0.695470 | 0.830664 | 00:00 |
| 32 | 1.233964 | 0.696157 | 0.829687 | 00:00 |
| 33 | 1.188573 | 0.695102 | 0.827734 | 00:00 |
| 34 | 1.147152 | 0.692032 | 0.830078 | 00:00 |
| 35 | 1.109321 | 0.692833 | 0.830274 | 00:00 |
| 36 | 1.074815 | 0.691245 | 0.830078 | 00:00 |
| 37 | 1.043194 | 0.690444 | 0.830664 | 00:00 |
| 38 | 1.014057 | 0.690498 | 0.830274 | 00:00 |
| 39 | 0.987654 | 0.690018 | 0.831055 | 00:00 |
| 40 | 0.963435 | 0.691070 | 0.829492 | 00:00 |
| 41 | 0.941620 | 0.689340 | 0.829297 | 00:00 |
| 42 | 0.921129 | 0.689264 | 0.829883 | 00:00 |
| 43 | 0.902168 | 0.688518 | 0.829687 | 00:00 |
| 44 | 0.885132 | 0.688177 | 0.830078 | 00:00 |
| 45 | 0.869137 | 0.688134 | 0.830469 | 00:00 |
| 46 | 0.854462 | 0.688108 | 0.829883 | 00:00 |
| 47 | 0.840988 | 0.688506 | 0.830078 | 00:00 |
| 48 | 0.828298 | 0.687414 | 0.830469 | 00:00 |
| 49 | 0.816715 | 0.687530 | 0.830664 | 00:00 |
| 50 | 0.806067 | 0.688392 | 0.829492 | 00:00 |
Paths: 474 , Tokens: 210 [49, 69, 93, 122, 183, 192] 0 Adj. Rand Index Score: 0.403137. Adj. Mutual Info Score: 0.473262. Classes: [0. 1. 2.] n_clusters: 3.0 Seed: 1234
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 5.360583 | 5.339934 | 0.000977 | 00:00 |
| 2 | 5.330885 | 5.322944 | 0.002148 | 00:00 |
| 3 | 5.291595 | 5.290174 | 0.030273 | 00:00 |
| 4 | 5.235236 | 5.231449 | 0.058203 | 00:00 |
| 5 | 5.161954 | 5.133492 | 0.199023 | 00:00 |
| 6 | 5.070352 | 4.973432 | 0.342969 | 00:00 |
| 7 | 4.960203 | 4.713956 | 0.431641 | 00:00 |
| 8 | 4.822572 | 4.296537 | 0.492969 | 00:00 |
| 9 | 4.653031 | 3.689778 | 0.573242 | 00:00 |
| 10 | 4.444983 | 2.947831 | 0.611523 | 00:00 |
| 11 | 4.201418 | 2.257740 | 0.637695 | 00:00 |
| 12 | 3.933572 | 1.764959 | 0.661133 | 00:00 |
| 13 | 3.663574 | 1.466589 | 0.689258 | 00:00 |
| 14 | 3.405512 | 1.272615 | 0.729688 | 00:00 |
| 15 | 3.163580 | 1.102663 | 0.747852 | 00:00 |
| 16 | 2.939159 | 0.979763 | 0.781836 | 00:00 |
| 17 | 2.731814 | 0.915451 | 0.784570 | 00:00 |
| 18 | 2.542495 | 0.838420 | 0.800000 | 00:00 |
| 19 | 2.371861 | 0.811591 | 0.816016 | 00:00 |
| 20 | 2.217978 | 0.766999 | 0.826172 | 00:00 |
| 21 | 2.078703 | 0.765001 | 0.815625 | 00:00 |
| 22 | 1.953796 | 0.729569 | 0.827734 | 00:00 |
| 23 | 1.842127 | 0.726283 | 0.821875 | 00:00 |
| 24 | 1.739573 | 0.711712 | 0.830664 | 00:00 |
| 25 | 1.647262 | 0.709510 | 0.829297 | 00:00 |
| 26 | 1.564008 | 0.706928 | 0.831836 | 00:00 |
| 27 | 1.488897 | 0.709796 | 0.829687 | 00:00 |
| 28 | 1.421902 | 0.703189 | 0.830273 | 00:00 |
| 29 | 1.360253 | 0.702853 | 0.830469 | 00:00 |
| 30 | 1.304384 | 0.698855 | 0.828516 | 00:00 |
| 31 | 1.253579 | 0.699284 | 0.830078 | 00:00 |
| 32 | 1.206853 | 0.698937 | 0.829492 | 00:00 |
| 33 | 1.163904 | 0.698271 | 0.830273 | 00:00 |
| 34 | 1.125033 | 0.695158 | 0.830859 | 00:00 |
| 35 | 1.089696 | 0.696452 | 0.829688 | 00:00 |
| 36 | 1.056884 | 0.694858 | 0.829883 | 00:00 |
| 37 | 1.026893 | 0.693264 | 0.829297 | 00:00 |
| 38 | 0.999946 | 0.692565 | 0.829688 | 00:00 |
| 39 | 0.973866 | 0.692132 | 0.828906 | 00:00 |
| 40 | 0.950946 | 0.690242 | 0.830859 | 00:00 |
| 41 | 0.929400 | 0.690247 | 0.830469 | 00:00 |
| 42 | 0.909856 | 0.689346 | 0.830273 | 00:00 |
| 43 | 0.892060 | 0.689736 | 0.829492 | 00:00 |
| 44 | 0.875426 | 0.689944 | 0.829883 | 00:00 |
| 45 | 0.860927 | 0.688841 | 0.828906 | 00:00 |
| 46 | 0.846746 | 0.688938 | 0.829492 | 00:00 |
| 47 | 0.834220 | 0.688983 | 0.828906 | 00:00 |
| 48 | 0.822524 | 0.689148 | 0.828906 | 00:00 |
| 49 | 0.812031 | 0.688785 | 0.829102 | 00:00 |
| 50 | 0.802275 | 0.689328 | 0.829883 | 00:00 |
Paths: 474 , Tokens: 210 [49, 69, 93, 122, 183, 192] 0 Adj. Rand Index Score: 0.403137. Adj. Mutual Info Score: 0.473262. Classes: [0. 1. 2.] n_clusters: 3.0 Seed: 43210
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 5.403941 | 5.347871 | 0.003320 | 00:00 |
| 2 | 5.378778 | 5.333846 | 0.006055 | 00:00 |
| 3 | 5.343394 | 5.307419 | 0.014844 | 00:00 |
| 4 | 5.295918 | 5.260590 | 0.111328 | 00:00 |
| 5 | 5.235258 | 5.178112 | 0.179102 | 00:00 |
| 6 | 5.159544 | 5.039396 | 0.241602 | 00:00 |
| 7 | 5.066237 | 4.809397 | 0.342188 | 00:00 |
| 8 | 4.949224 | 4.430661 | 0.452930 | 00:00 |
| 9 | 4.802004 | 3.868149 | 0.528320 | 00:00 |
| 10 | 4.615366 | 3.131550 | 0.598633 | 00:00 |
| 11 | 4.388020 | 2.404979 | 0.621484 | 00:00 |
| 12 | 4.127118 | 1.895180 | 0.649805 | 00:00 |
| 13 | 3.854105 | 1.570137 | 0.679883 | 00:00 |
| 14 | 3.583565 | 1.320367 | 0.725195 | 00:00 |
| 15 | 3.325577 | 1.153053 | 0.729883 | 00:00 |
| 16 | 3.085616 | 1.017287 | 0.767383 | 00:00 |
| 17 | 2.864123 | 0.921526 | 0.797656 | 00:00 |
| 18 | 2.663930 | 0.876225 | 0.797266 | 00:00 |
| 19 | 2.482517 | 0.814312 | 0.805859 | 00:00 |
| 20 | 2.318162 | 0.787636 | 0.818359 | 00:00 |
| 21 | 2.169858 | 0.755756 | 0.821289 | 00:00 |
| 22 | 2.037002 | 0.754840 | 0.825391 | 00:00 |
| 23 | 1.917169 | 0.728547 | 0.829297 | 00:00 |
| 24 | 1.810569 | 0.718751 | 0.827539 | 00:00 |
| 25 | 1.712431 | 0.712950 | 0.828711 | 00:00 |
| 26 | 1.623648 | 0.707994 | 0.829883 | 00:00 |
| 27 | 1.543457 | 0.706042 | 0.829492 | 00:00 |
| 28 | 1.471211 | 0.701216 | 0.830469 | 00:00 |
| 29 | 1.405039 | 0.701672 | 0.829102 | 00:00 |
| 30 | 1.345936 | 0.700136 | 0.829492 | 00:00 |
| 31 | 1.291155 | 0.700267 | 0.829688 | 00:00 |
| 32 | 1.241089 | 0.695997 | 0.829102 | 00:00 |
| 33 | 1.195853 | 0.694288 | 0.830273 | 00:00 |
| 34 | 1.153128 | 0.695048 | 0.828906 | 00:00 |
| 35 | 1.114974 | 0.692655 | 0.829492 | 00:00 |
| 36 | 1.080334 | 0.693584 | 0.829687 | 00:00 |
| 37 | 1.048855 | 0.691977 | 0.831250 | 00:00 |
| 38 | 1.019418 | 0.691125 | 0.830664 | 00:00 |
| 39 | 0.992529 | 0.691195 | 0.830859 | 00:00 |
| 40 | 0.968026 | 0.690824 | 0.829492 | 00:00 |
| 41 | 0.945965 | 0.690885 | 0.830078 | 00:00 |
| 42 | 0.925019 | 0.689207 | 0.830078 | 00:00 |
| 43 | 0.906437 | 0.690311 | 0.828711 | 00:00 |
| 44 | 0.888516 | 0.689050 | 0.829883 | 00:00 |
| 45 | 0.872950 | 0.688744 | 0.830859 | 00:00 |
| 46 | 0.857661 | 0.688223 | 0.830469 | 00:00 |
| 47 | 0.843969 | 0.688832 | 0.830078 | 00:00 |
| 48 | 0.831302 | 0.689144 | 0.829687 | 00:00 |
| 49 | 0.819841 | 0.688164 | 0.829883 | 00:00 |
| 50 | 0.809521 | 0.687599 | 0.830469 | 00:00 |
Paths: 474 , Tokens: 210 [49, 69, 93, 122, 183, 192] 0 Adj. Rand Index Score: 0.403137. Adj. Mutual Info Score: 0.473262. Classes: [0. 1. 2.] n_clusters: 3.0 Seed: 1133557799
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 5.365668 | 5.331084 | 0.009180 | 00:00 |
| 2 | 5.340348 | 5.317561 | 0.046484 | 00:00 |
| 3 | 5.306249 | 5.291880 | 0.082031 | 00:00 |
| 4 | 5.259461 | 5.245383 | 0.136914 | 00:00 |
| 5 | 5.196848 | 5.162170 | 0.213477 | 00:00 |
| 6 | 5.114454 | 5.017547 | 0.293945 | 00:00 |
| 7 | 5.013210 | 4.776783 | 0.347461 | 00:00 |
| 8 | 4.888155 | 4.399590 | 0.386523 | 00:00 |
| 9 | 4.731781 | 3.819297 | 0.465234 | 00:00 |
| 10 | 4.538999 | 3.103232 | 0.509570 | 00:00 |
| 11 | 4.310895 | 2.425593 | 0.593750 | 00:00 |
| 12 | 4.050942 | 1.886504 | 0.647070 | 00:00 |
| 13 | 3.780839 | 1.519994 | 0.697070 | 00:00 |
| 14 | 3.514572 | 1.264139 | 0.732031 | 00:00 |
| 15 | 3.259416 | 1.091474 | 0.762109 | 00:00 |
| 16 | 3.023930 | 0.969932 | 0.784570 | 00:00 |
| 17 | 2.807616 | 0.889857 | 0.795508 | 00:00 |
| 18 | 2.612500 | 0.839281 | 0.795508 | 00:00 |
| 19 | 2.433778 | 0.781845 | 0.820898 | 00:00 |
| 20 | 2.272805 | 0.773382 | 0.823242 | 00:00 |
| 21 | 2.127784 | 0.740472 | 0.815039 | 00:00 |
| 22 | 1.998613 | 0.727559 | 0.828320 | 00:00 |
| 23 | 1.881709 | 0.722701 | 0.828711 | 00:00 |
| 24 | 1.776437 | 0.718765 | 0.828906 | 00:00 |
| 25 | 1.681547 | 0.712888 | 0.829492 | 00:00 |
| 26 | 1.594938 | 0.710307 | 0.829297 | 00:00 |
| 27 | 1.517329 | 0.704999 | 0.829102 | 00:00 |
| 28 | 1.447304 | 0.703568 | 0.828711 | 00:00 |
| 29 | 1.383166 | 0.702020 | 0.828906 | 00:00 |
| 30 | 1.325160 | 0.701303 | 0.823633 | 00:00 |
| 31 | 1.272173 | 0.698047 | 0.828711 | 00:00 |
| 32 | 1.223696 | 0.697405 | 0.829492 | 00:00 |
| 33 | 1.179137 | 0.695103 | 0.829688 | 00:00 |
| 34 | 1.138323 | 0.697161 | 0.829492 | 00:00 |
| 35 | 1.101286 | 0.694273 | 0.827930 | 00:00 |
| 36 | 1.068279 | 0.694621 | 0.829297 | 00:00 |
| 37 | 1.037236 | 0.692784 | 0.830469 | 00:00 |
| 38 | 1.009717 | 0.691223 | 0.829297 | 00:00 |
| 39 | 0.983269 | 0.691011 | 0.829297 | 00:00 |
| 40 | 0.960620 | 0.690857 | 0.829297 | 00:00 |
| 41 | 0.938687 | 0.691033 | 0.830078 | 00:00 |
| 42 | 0.918546 | 0.690428 | 0.829297 | 00:00 |
| 43 | 0.899595 | 0.689768 | 0.829102 | 00:00 |
| 44 | 0.882471 | 0.689845 | 0.829492 | 00:00 |
| 45 | 0.866615 | 0.689251 | 0.828906 | 00:00 |
| 46 | 0.851530 | 0.689187 | 0.830664 | 00:00 |
| 47 | 0.838332 | 0.688867 | 0.829688 | 00:00 |
| 48 | 0.825267 | 0.688783 | 0.830664 | 00:00 |
| 49 | 0.814548 | 0.688674 | 0.829102 | 00:00 |
| 50 | 0.803670 | 0.688441 | 0.830078 | 00:00 |
Paths: 474 , Tokens: 210 [49, 69, 93, 122, 183, 192] 0 Adj. Rand Index Score: 0.403137. Adj. Mutual Info Score: 0.473262. Classes: [0. 1. 2.] n_clusters: 3.0 Seed: 22446688
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 5.441226 | 5.360204 | 0.005273 | 00:00 |
| 2 | 5.405838 | 5.344580 | 0.006055 | 00:00 |
| 3 | 5.362249 | 5.313320 | 0.009180 | 00:00 |
| 4 | 5.301203 | 5.253747 | 0.045313 | 00:00 |
| 5 | 5.226854 | 5.152664 | 0.126758 | 00:00 |
| 6 | 5.135906 | 4.990287 | 0.211328 | 00:00 |
| 7 | 5.023493 | 4.728133 | 0.373438 | 00:00 |
| 8 | 4.885191 | 4.299979 | 0.457617 | 00:00 |
| 9 | 4.708629 | 3.662229 | 0.516406 | 00:00 |
| 10 | 4.494712 | 2.872623 | 0.598633 | 00:00 |
| 11 | 4.244754 | 2.215041 | 0.641016 | 00:00 |
| 12 | 3.972751 | 1.734400 | 0.677734 | 00:00 |
| 13 | 3.699681 | 1.432288 | 0.701172 | 00:00 |
| 14 | 3.436446 | 1.212319 | 0.744531 | 00:00 |
| 15 | 3.185848 | 1.052064 | 0.767383 | 00:00 |
| 16 | 2.956450 | 0.937611 | 0.787305 | 00:00 |
| 17 | 2.744856 | 0.876109 | 0.802344 | 00:00 |
| 18 | 2.553393 | 0.813987 | 0.801172 | 00:00 |
| 19 | 2.380120 | 0.784980 | 0.823633 | 00:00 |
| 20 | 2.224722 | 0.754113 | 0.818164 | 00:00 |
| 21 | 2.083665 | 0.732631 | 0.827930 | 00:00 |
| 22 | 1.957701 | 0.719833 | 0.828125 | 00:00 |
| 23 | 1.844541 | 0.714806 | 0.829688 | 00:00 |
| 24 | 1.742213 | 0.713624 | 0.829297 | 00:00 |
| 25 | 1.651020 | 0.709259 | 0.818945 | 00:00 |
| 26 | 1.568126 | 0.707530 | 0.829492 | 00:00 |
| 27 | 1.492600 | 0.705656 | 0.828711 | 00:00 |
| 28 | 1.424289 | 0.702649 | 0.829102 | 00:00 |
| 29 | 1.362254 | 0.700709 | 0.829297 | 00:00 |
| 30 | 1.305769 | 0.698122 | 0.830273 | 00:00 |
| 31 | 1.253935 | 0.698801 | 0.829102 | 00:00 |
| 32 | 1.207279 | 0.698146 | 0.829297 | 00:00 |
| 33 | 1.164430 | 0.693108 | 0.830078 | 00:00 |
| 34 | 1.125676 | 0.694397 | 0.829492 | 00:00 |
| 35 | 1.090790 | 0.695613 | 0.828906 | 00:00 |
| 36 | 1.057644 | 0.693047 | 0.829297 | 00:00 |
| 37 | 1.028132 | 0.692982 | 0.830664 | 00:00 |
| 38 | 1.000291 | 0.692366 | 0.829492 | 00:00 |
| 39 | 0.975141 | 0.691881 | 0.829687 | 00:00 |
| 40 | 0.952435 | 0.691338 | 0.829883 | 00:00 |
| 41 | 0.932107 | 0.690238 | 0.829492 | 00:00 |
| 42 | 0.912812 | 0.690793 | 0.829687 | 00:00 |
| 43 | 0.894672 | 0.689931 | 0.829492 | 00:00 |
| 44 | 0.877837 | 0.690086 | 0.828320 | 00:00 |
| 45 | 0.863422 | 0.689874 | 0.829297 | 00:00 |
| 46 | 0.849760 | 0.688762 | 0.828906 | 00:00 |
| 47 | 0.836286 | 0.689570 | 0.829297 | 00:00 |
| 48 | 0.824444 | 0.690024 | 0.829102 | 00:00 |
| 49 | 0.813577 | 0.690119 | 0.828906 | 00:00 |
| 50 | 0.803005 | 0.689136 | 0.828516 | 00:00 |
Paths: 474 , Tokens: 210 [49, 69, 93, 122, 183, 192] 0 Adj. Rand Index Score: 0.403137. Adj. Mutual Info Score: 0.473262. Classes: [0. 1. 2.] n_clusters: 3.0 Seed: 123456789
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 5.416296 | 5.326296 | 0.012891 | 00:00 |
| 2 | 5.382298 | 5.311406 | 0.014258 | 00:00 |
| 3 | 5.335956 | 5.281637 | 0.051563 | 00:00 |
| 4 | 5.271511 | 5.225453 | 0.115234 | 00:00 |
| 5 | 5.192565 | 5.126373 | 0.224414 | 00:00 |
| 6 | 5.095237 | 4.954106 | 0.274805 | 00:00 |
| 7 | 4.980387 | 4.675584 | 0.327539 | 00:00 |
| 8 | 4.844080 | 4.250974 | 0.402734 | 00:00 |
| 9 | 4.676033 | 3.656956 | 0.477539 | 00:00 |
| 10 | 4.472655 | 2.943804 | 0.550391 | 00:00 |
| 11 | 4.236574 | 2.291404 | 0.600195 | 00:00 |
| 12 | 3.976966 | 1.823546 | 0.641797 | 00:00 |
| 13 | 3.709702 | 1.508870 | 0.682227 | 00:00 |
| 14 | 3.448513 | 1.264695 | 0.726953 | 00:00 |
| 15 | 3.202489 | 1.081352 | 0.761914 | 00:00 |
| 16 | 2.971808 | 0.957598 | 0.786523 | 00:00 |
| 17 | 2.759957 | 0.884779 | 0.802734 | 00:00 |
| 18 | 2.568247 | 0.832163 | 0.798633 | 00:00 |
| 19 | 2.393802 | 0.783710 | 0.819141 | 00:00 |
| 20 | 2.237143 | 0.761100 | 0.824219 | 00:00 |
| 21 | 2.095860 | 0.747342 | 0.808008 | 00:00 |
| 22 | 1.969106 | 0.728888 | 0.826563 | 00:00 |
| 23 | 1.855531 | 0.721163 | 0.828516 | 00:00 |
| 24 | 1.751959 | 0.720848 | 0.815234 | 00:00 |
| 25 | 1.657961 | 0.714446 | 0.828711 | 00:00 |
| 26 | 1.574122 | 0.709394 | 0.828320 | 00:00 |
| 27 | 1.498112 | 0.708745 | 0.829883 | 00:00 |
| 28 | 1.429204 | 0.704829 | 0.828906 | 00:00 |
| 29 | 1.366304 | 0.704065 | 0.829297 | 00:00 |
| 30 | 1.309739 | 0.701667 | 0.828125 | 00:00 |
| 31 | 1.258214 | 0.702036 | 0.828711 | 00:00 |
| 32 | 1.211292 | 0.699824 | 0.829883 | 00:00 |
| 33 | 1.168059 | 0.699838 | 0.829297 | 00:00 |
| 34 | 1.128668 | 0.698947 | 0.829687 | 00:00 |
| 35 | 1.092727 | 0.697827 | 0.829297 | 00:00 |
| 36 | 1.060246 | 0.695364 | 0.829688 | 00:00 |
| 37 | 1.030234 | 0.696152 | 0.829492 | 00:00 |
| 38 | 1.003003 | 0.694364 | 0.829492 | 00:00 |
| 39 | 0.977798 | 0.693920 | 0.829687 | 00:00 |
| 40 | 0.954657 | 0.692629 | 0.830273 | 00:00 |
| 41 | 0.933317 | 0.692392 | 0.830078 | 00:00 |
| 42 | 0.913273 | 0.692883 | 0.831055 | 00:00 |
| 43 | 0.895107 | 0.692011 | 0.829883 | 00:00 |
| 44 | 0.878877 | 0.692891 | 0.828516 | 00:00 |
| 45 | 0.863623 | 0.691781 | 0.829883 | 00:00 |
| 46 | 0.849281 | 0.692010 | 0.829102 | 00:00 |
| 47 | 0.836306 | 0.692107 | 0.829492 | 00:00 |
| 48 | 0.824154 | 0.691976 | 0.828906 | 00:00 |
| 49 | 0.813400 | 0.692439 | 0.829102 | 00:00 |
| 50 | 0.803464 | 0.692237 | 0.828125 | 00:00 |
Paths: 474 , Tokens: 210 [49, 69, 93, 122, 183, 192] 0 Adj. Rand Index Score: 0.403137. Adj. Mutual Info Score: 0.473262. Classes: [0. 1. 2.] n_clusters: 3.0 Seed: 987654321
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 5.426015 | 5.362936 | 0.003320 | 00:00 |
| 2 | 5.400373 | 5.350706 | 0.024414 | 00:00 |
| 3 | 5.366880 | 5.324727 | 0.034961 | 00:00 |
| 4 | 5.319046 | 5.275183 | 0.060938 | 00:00 |
| 5 | 5.256995 | 5.185686 | 0.113672 | 00:00 |
| 6 | 5.179133 | 5.033902 | 0.227539 | 00:00 |
| 7 | 5.083931 | 4.793162 | 0.337500 | 00:00 |
| 8 | 4.966156 | 4.427274 | 0.442187 | 00:00 |
| 9 | 4.817691 | 3.873680 | 0.512305 | 00:00 |
| 10 | 4.627573 | 3.151974 | 0.582813 | 00:00 |
| 11 | 4.397860 | 2.444832 | 0.604102 | 00:00 |
| 12 | 4.132672 | 1.880567 | 0.629883 | 00:00 |
| 13 | 3.857416 | 1.538617 | 0.677734 | 00:00 |
| 14 | 3.586134 | 1.299754 | 0.724023 | 00:00 |
| 15 | 3.328479 | 1.111829 | 0.761133 | 00:00 |
| 16 | 3.088239 | 0.980855 | 0.783789 | 00:00 |
| 17 | 2.866111 | 0.887885 | 0.800195 | 00:00 |
| 18 | 2.662750 | 0.847547 | 0.813086 | 00:00 |
| 19 | 2.479776 | 0.793298 | 0.822461 | 00:00 |
| 20 | 2.314445 | 0.756062 | 0.826172 | 00:00 |
| 21 | 2.165126 | 0.752000 | 0.828516 | 00:00 |
| 22 | 2.032464 | 0.732467 | 0.818945 | 00:00 |
| 23 | 1.912521 | 0.726006 | 0.829102 | 00:00 |
| 24 | 1.804305 | 0.717765 | 0.828906 | 00:00 |
| 25 | 1.706875 | 0.707807 | 0.829688 | 00:00 |
| 26 | 1.618619 | 0.711433 | 0.830859 | 00:00 |
| 27 | 1.540354 | 0.710051 | 0.828516 | 00:00 |
| 28 | 1.468145 | 0.702537 | 0.830664 | 00:00 |
| 29 | 1.402957 | 0.701645 | 0.828906 | 00:00 |
| 30 | 1.342520 | 0.702802 | 0.830469 | 00:00 |
| 31 | 1.288029 | 0.699983 | 0.828906 | 00:00 |
| 32 | 1.238224 | 0.697453 | 0.830469 | 00:00 |
| 33 | 1.192025 | 0.695717 | 0.828711 | 00:00 |
| 34 | 1.150905 | 0.694930 | 0.828711 | 00:00 |
| 35 | 1.111843 | 0.695267 | 0.830273 | 00:00 |
| 36 | 1.076876 | 0.692932 | 0.830469 | 00:00 |
| 37 | 1.045596 | 0.692977 | 0.827930 | 00:00 |
| 38 | 1.016590 | 0.692284 | 0.829492 | 00:00 |
| 39 | 0.990128 | 0.690923 | 0.830078 | 00:00 |
| 40 | 0.965581 | 0.691650 | 0.830469 | 00:00 |
| 41 | 0.943066 | 0.690684 | 0.830078 | 00:00 |
| 42 | 0.923258 | 0.691212 | 0.829102 | 00:00 |
| 43 | 0.904686 | 0.690002 | 0.829492 | 00:00 |
| 44 | 0.886782 | 0.690057 | 0.828906 | 00:00 |
| 45 | 0.870754 | 0.688621 | 0.829492 | 00:00 |
| 46 | 0.856111 | 0.688932 | 0.829688 | 00:00 |
| 47 | 0.842217 | 0.689372 | 0.830469 | 00:00 |
| 48 | 0.829881 | 0.688909 | 0.830078 | 00:00 |
| 49 | 0.819214 | 0.689368 | 0.830859 | 00:00 |
| 50 | 0.808700 | 0.689581 | 0.830469 | 00:00 |
Paths: 474 , Tokens: 210 [49, 69, 93, 122, 183, 192] Adj. Rand Index Score: 0.403137. Adj. Mutual Info Score: 0.473262. Classes: [0. 1. 2.] n_clusters: 3.0 Seed: 86420
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 5.367269 | 5.319778 | 0.009570 | 00:00 |
| 2 | 5.337466 | 5.306462 | 0.027344 | 00:00 |
| 3 | 5.300839 | 5.279361 | 0.039844 | 00:00 |
| 4 | 5.252572 | 5.229908 | 0.061523 | 00:00 |
| 5 | 5.190369 | 5.144810 | 0.134180 | 00:00 |
| 6 | 5.114606 | 5.003541 | 0.287891 | 00:00 |
| 7 | 5.018514 | 4.766831 | 0.422461 | 00:00 |
| 8 | 4.897010 | 4.386443 | 0.489258 | 00:00 |
| 9 | 4.741733 | 3.815784 | 0.550195 | 00:00 |
| 10 | 4.544442 | 3.059375 | 0.590820 | 00:00 |
| 11 | 4.306994 | 2.337376 | 0.634375 | 00:00 |
| 12 | 4.038640 | 1.819747 | 0.666797 | 00:00 |
| 13 | 3.762982 | 1.477777 | 0.703906 | 00:00 |
| 14 | 3.492625 | 1.256381 | 0.737305 | 00:00 |
| 15 | 3.237948 | 1.079919 | 0.768359 | 00:00 |
| 16 | 3.003993 | 0.979269 | 0.778320 | 00:00 |
| 17 | 2.788758 | 0.881393 | 0.803906 | 00:00 |
| 18 | 2.593487 | 0.828739 | 0.797852 | 00:00 |
| 19 | 2.418019 | 0.772036 | 0.823633 | 00:00 |
| 20 | 2.257862 | 0.752017 | 0.815625 | 00:00 |
| 21 | 2.115612 | 0.747274 | 0.821680 | 00:00 |
| 22 | 1.987264 | 0.730523 | 0.829492 | 00:00 |
| 23 | 1.871745 | 0.720253 | 0.828516 | 00:00 |
| 24 | 1.767492 | 0.716288 | 0.829883 | 00:00 |
| 25 | 1.673183 | 0.708675 | 0.829687 | 00:00 |
| 26 | 1.586771 | 0.706825 | 0.829297 | 00:00 |
| 27 | 1.509312 | 0.703524 | 0.828711 | 00:00 |
| 28 | 1.438722 | 0.701848 | 0.830078 | 00:00 |
| 29 | 1.375632 | 0.697826 | 0.829492 | 00:00 |
| 30 | 1.318375 | 0.695348 | 0.829492 | 00:00 |
| 31 | 1.265612 | 0.696183 | 0.830273 | 00:00 |
| 32 | 1.218103 | 0.695618 | 0.830273 | 00:00 |
| 33 | 1.174246 | 0.692027 | 0.829297 | 00:00 |
| 34 | 1.134149 | 0.694777 | 0.829102 | 00:00 |
| 35 | 1.097459 | 0.692303 | 0.829883 | 00:00 |
| 36 | 1.064309 | 0.690151 | 0.831250 | 00:00 |
| 37 | 1.033359 | 0.690535 | 0.830078 | 00:00 |
| 38 | 1.005856 | 0.691171 | 0.829883 | 00:00 |
| 39 | 0.980026 | 0.689470 | 0.829688 | 00:00 |
| 40 | 0.956219 | 0.691054 | 0.831250 | 00:00 |
| 41 | 0.934772 | 0.689609 | 0.830469 | 00:00 |
| 42 | 0.914801 | 0.689795 | 0.829492 | 00:00 |
| 43 | 0.896468 | 0.689283 | 0.829297 | 00:00 |
| 44 | 0.878961 | 0.688875 | 0.828906 | 00:00 |
| 45 | 0.863868 | 0.688404 | 0.829687 | 00:00 |
| 46 | 0.849902 | 0.688451 | 0.828906 | 00:00 |
| 47 | 0.837169 | 0.687207 | 0.829688 | 00:00 |
| 48 | 0.824529 | 0.687741 | 0.830469 | 00:00 |
| 49 | 0.813520 | 0.688252 | 0.829883 | 00:00 |
| 50 | 0.803252 | 0.688156 | 0.830274 | 00:00 |
Paths: 474 , Tokens: 210 [49, 69, 93, 122, 183, 192] 0 Adj. Rand Index Score: 0.403137. Adj. Mutual Info Score: 0.473262. Classes: [0. 1. 2.] n_clusters: 3.0 File Name to Train: data_sets/01_bunch/phoneme/phoneme.csv ############################################################# HDBSCAN ############################################################# Adj. Rand Index Score: 0.760092. Adj. Mutual Info Score: 0.779888. Classes: [0 1 2 3] n_clusters: 4 QSHIFTPP ############################################################# Adj. Rand Index Score: 0.489517. Adj. Mutual Info Score: 0.584644. Classes: [ 0 1 2 3 4 5 6 7 8 9 10] n_clusters: 11 HQSHIFT ############################################################# Adj. Rand Index Score: 0.760092. Adj. Mutual Info Score: 0.779888. Classes: [0 1 2 3] n_clusters: 4 Seed: 0
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 6.316370 | 5.808015 | 0.424561 | 00:04 |
| 2 | 3.060650 | 2.653844 | 0.628309 | 00:04 |
| 3 | 1.569017 | 1.378768 | 0.787614 | 00:04 |
| 4 | 0.879988 | 0.798094 | 0.870250 | 00:04 |
| 5 | 0.626892 | 0.601034 | 0.883298 | 00:04 |
| 6 | 0.549547 | 0.527949 | 0.908386 | 00:04 |
| 7 | 0.524002 | 0.531690 | 0.910930 | 00:04 |
| 8 | 0.517110 | 0.518561 | 0.910351 | 00:04 |
| 9 | 0.515187 | 0.521524 | 0.910724 | 00:04 |
| 10 | 0.511016 | 0.519984 | 0.905259 | 00:04 |
| 11 | 0.512947 | 0.509495 | 0.908355 | 00:04 |
| 12 | 0.506702 | 0.508393 | 0.909645 | 00:04 |
| 13 | 0.508885 | 0.504852 | 0.911018 | 00:04 |
| 14 | 0.502568 | 0.502486 | 0.910706 | 00:04 |
| 15 | 0.508006 | 0.505523 | 0.909136 | 00:04 |
| 16 | 0.507032 | 0.500764 | 0.910400 | 00:04 |
| 17 | 0.502466 | 0.499461 | 0.911488 | 00:04 |
| 18 | 0.502905 | 0.498913 | 0.911298 | 00:04 |
| 19 | 0.498783 | 0.498541 | 0.911420 | 00:04 |
| 20 | 0.502103 | 0.497018 | 0.911420 | 00:04 |
| 21 | 0.499833 | 0.497825 | 0.911435 | 00:04 |
| 22 | 0.503335 | 0.497198 | 0.911451 | 00:04 |
| 23 | 0.497524 | 0.496449 | 0.911475 | 00:04 |
| 24 | 0.502424 | 0.496368 | 0.911482 | 00:04 |
| 25 | 0.500189 | 0.496824 | 0.911420 | 00:04 |
| 26 | 0.498448 | 0.496626 | 0.911503 | 00:04 |
| 27 | 0.498796 | 0.496462 | 0.911503 | 00:04 |
| 28 | 0.498102 | 0.495644 | 0.911516 | 00:04 |
| 29 | 0.498780 | 0.495936 | 0.911506 | 00:04 |
| 30 | 0.495902 | 0.495727 | 0.911509 | 00:04 |
| 31 | 0.497488 | 0.495989 | 0.911548 | 00:04 |
| 32 | 0.497862 | 0.495419 | 0.911537 | 00:04 |
| 33 | 0.497809 | 0.495314 | 0.911532 | 00:04 |
| 34 | 0.495247 | 0.495368 | 0.911521 | 00:04 |
| 35 | 0.493432 | 0.495353 | 0.911532 | 00:04 |
| 36 | 0.494336 | 0.495117 | 0.911519 | 00:04 |
| 37 | 0.494129 | 0.495244 | 0.911524 | 00:04 |
| 38 | 0.500305 | 0.495016 | 0.911521 | 00:04 |
| 39 | 0.497980 | 0.494890 | 0.911521 | 00:04 |
| 40 | 0.495104 | 0.494889 | 0.911516 | 00:04 |
| 41 | 0.498446 | 0.494922 | 0.911519 | 00:04 |
| 42 | 0.488165 | 0.494842 | 0.911521 | 00:04 |
| 43 | 0.494024 | 0.494668 | 0.911524 | 00:04 |
| 44 | 0.493174 | 0.494684 | 0.911519 | 00:04 |
| 45 | 0.491586 | 0.494710 | 0.911532 | 00:04 |
| 46 | 0.494835 | 0.494646 | 0.911532 | 00:04 |
| 47 | 0.493789 | 0.494604 | 0.911529 | 00:04 |
| 48 | 0.494644 | 0.494585 | 0.911516 | 00:04 |
| 49 | 0.493714 | 0.494571 | 0.911527 | 00:04 |
| 50 | 0.492076 | 0.494580 | 0.911527 | 00:04 |
/home/can/anaconda3/envs/hqshift/lib/python3.6/site-packages/fastai/datasets.py:153: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details. with open(fpath, 'r') as yaml_file: return yaml.load(yaml_file)
Paths: 19283 , Tokens: 4508 [108, 385, 419, 1116, 2174, 2396, 2751, 2869, 3200, 3409, 3724, 3890, 4023, 4174, 4374, 4411, 4425] Adj. Rand Index Score: 0.758399. Adj. Mutual Info Score: 0.777229. Classes: [0. 1. 2. 3.] n_clusters: 4.0 Seed: 23
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 6.574629 | 6.074909 | 0.429506 | 00:04 |
| 2 | 3.216560 | 2.813805 | 0.621636 | 00:04 |
| 3 | 1.613814 | 1.430764 | 0.781130 | 00:04 |
| 4 | 0.901068 | 0.808517 | 0.863429 | 00:04 |
| 5 | 0.629393 | 0.595092 | 0.898019 | 00:04 |
| 6 | 0.545325 | 0.531476 | 0.908884 | 00:04 |
| 7 | 0.522726 | 0.518444 | 0.909894 | 00:04 |
| 8 | 0.516012 | 0.517517 | 0.904140 | 00:04 |
| 9 | 0.515197 | 0.512823 | 0.911402 | 00:04 |
| 10 | 0.508616 | 0.508320 | 0.910790 | 00:04 |
| 11 | 0.511817 | 0.506427 | 0.910805 | 00:04 |
| 12 | 0.506736 | 0.504520 | 0.911046 | 00:04 |
| 13 | 0.502374 | 0.502506 | 0.911101 | 00:04 |
| 14 | 0.504862 | 0.502608 | 0.911478 | 00:04 |
| 15 | 0.503657 | 0.502396 | 0.911472 | 00:04 |
| 16 | 0.503264 | 0.500326 | 0.911477 | 00:04 |
| 17 | 0.499323 | 0.499110 | 0.911501 | 00:04 |
| 18 | 0.504980 | 0.500454 | 0.911454 | 00:04 |
| 19 | 0.505993 | 0.498994 | 0.911457 | 00:04 |
| 20 | 0.502101 | 0.498015 | 0.911464 | 00:04 |
| 21 | 0.497188 | 0.497319 | 0.911509 | 00:04 |
| 22 | 0.496976 | 0.497415 | 0.911459 | 00:04 |
| 23 | 0.503793 | 0.497564 | 0.910867 | 00:04 |
| 24 | 0.494184 | 0.496543 | 0.911506 | 00:04 |
| 25 | 0.502126 | 0.495855 | 0.911521 | 00:04 |
| 26 | 0.497916 | 0.496307 | 0.911532 | 00:04 |
| 27 | 0.500842 | 0.496016 | 0.911532 | 00:04 |
| 28 | 0.498431 | 0.496234 | 0.911535 | 00:04 |
| 29 | 0.495241 | 0.495859 | 0.911537 | 00:04 |
| 30 | 0.498878 | 0.496075 | 0.911519 | 00:04 |
| 31 | 0.492436 | 0.495340 | 0.911524 | 00:04 |
| 32 | 0.497832 | 0.495400 | 0.911534 | 00:04 |
| 33 | 0.498935 | 0.495298 | 0.911519 | 00:04 |
| 34 | 0.495751 | 0.495553 | 0.911524 | 00:04 |
| 35 | 0.498347 | 0.495156 | 0.911521 | 00:04 |
| 36 | 0.499127 | 0.495370 | 0.911530 | 00:04 |
| 37 | 0.499041 | 0.495368 | 0.911524 | 00:04 |
| 38 | 0.495347 | 0.495472 | 0.911524 | 00:04 |
| 39 | 0.494864 | 0.494883 | 0.911519 | 00:04 |
| 40 | 0.491581 | 0.494834 | 0.911519 | 00:04 |
| 41 | 0.494969 | 0.494946 | 0.911527 | 00:04 |
| 42 | 0.501502 | 0.495000 | 0.911535 | 00:04 |
| 43 | 0.495103 | 0.494698 | 0.911521 | 00:04 |
| 44 | 0.495817 | 0.494894 | 0.911521 | 00:04 |
| 45 | 0.494023 | 0.494670 | 0.911521 | 00:04 |
| 46 | 0.491549 | 0.494622 | 0.911526 | 00:04 |
| 47 | 0.498099 | 0.494614 | 0.911516 | 00:04 |
| 48 | 0.493214 | 0.494612 | 0.911521 | 00:04 |
| 49 | 0.496769 | 0.494597 | 0.911535 | 00:04 |
| 50 | 0.495355 | 0.494619 | 0.911516 | 00:04 |
Paths: 19283 , Tokens: 4508 [108, 385, 419, 1116, 2174, 2396, 2751, 2869, 3200, 3409, 3724, 3890, 4023, 4174, 4374, 4411, 4425] Adj. Rand Index Score: 0.758399. Adj. Mutual Info Score: 0.777229. Classes: [0. 1. 2. 3.] n_clusters: 4.0 Seed: 42
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 6.407751 | 5.903373 | 0.426181 | 00:04 |
| 2 | 3.089772 | 2.699888 | 0.632766 | 00:04 |
| 3 | 1.571529 | 1.408835 | 0.784367 | 00:04 |
| 4 | 0.876988 | 0.800000 | 0.870069 | 00:04 |
| 5 | 0.633197 | 0.591254 | 0.897023 | 00:04 |
| 6 | 0.541815 | 0.537480 | 0.909251 | 00:04 |
| 7 | 0.531185 | 0.524764 | 0.911083 | 00:04 |
| 8 | 0.515980 | 0.522101 | 0.902769 | 00:04 |
| 9 | 0.518969 | 0.513482 | 0.911384 | 00:04 |
| 10 | 0.508358 | 0.509831 | 0.911026 | 00:04 |
| 11 | 0.509080 | 0.508642 | 0.911337 | 00:04 |
| 12 | 0.508187 | 0.508477 | 0.911397 | 00:04 |
| 13 | 0.512016 | 0.505714 | 0.911267 | 00:04 |
| 14 | 0.504984 | 0.504177 | 0.910694 | 00:04 |
| 15 | 0.503389 | 0.499772 | 0.911425 | 00:04 |
| 16 | 0.501744 | 0.498861 | 0.911436 | 00:04 |
| 17 | 0.504885 | 0.500235 | 0.911487 | 00:04 |
| 18 | 0.504032 | 0.498135 | 0.911472 | 00:04 |
| 19 | 0.502422 | 0.497629 | 0.911500 | 00:04 |
| 20 | 0.502267 | 0.498413 | 0.910462 | 00:04 |
| 21 | 0.499621 | 0.498334 | 0.910740 | 00:04 |
| 22 | 0.498938 | 0.497503 | 0.911500 | 00:04 |
| 23 | 0.497962 | 0.497379 | 0.911405 | 00:04 |
| 24 | 0.497507 | 0.496922 | 0.911482 | 00:04 |
| 25 | 0.502372 | 0.497171 | 0.911506 | 00:04 |
| 26 | 0.499433 | 0.496359 | 0.911524 | 00:04 |
| 27 | 0.501449 | 0.495919 | 0.911532 | 00:04 |
| 28 | 0.497380 | 0.495587 | 0.911532 | 00:04 |
| 29 | 0.503203 | 0.495575 | 0.911540 | 00:04 |
| 30 | 0.494966 | 0.495803 | 0.911524 | 00:04 |
| 31 | 0.498759 | 0.495334 | 0.911532 | 00:04 |
| 32 | 0.500002 | 0.495564 | 0.911513 | 00:04 |
| 33 | 0.498622 | 0.496321 | 0.911537 | 00:04 |
| 34 | 0.498460 | 0.495512 | 0.911553 | 00:04 |
| 35 | 0.494736 | 0.495297 | 0.911532 | 00:04 |
| 36 | 0.497079 | 0.494899 | 0.911540 | 00:04 |
| 37 | 0.497569 | 0.495220 | 0.911545 | 00:04 |
| 38 | 0.495902 | 0.495036 | 0.911521 | 00:04 |
| 39 | 0.495797 | 0.494849 | 0.911524 | 00:04 |
| 40 | 0.493933 | 0.494966 | 0.911519 | 00:04 |
| 41 | 0.492406 | 0.494990 | 0.911521 | 00:04 |
| 42 | 0.495971 | 0.494784 | 0.911532 | 00:04 |
| 43 | 0.491768 | 0.494750 | 0.911519 | 00:04 |
| 44 | 0.490600 | 0.494740 | 0.911521 | 00:04 |
| 45 | 0.492189 | 0.494635 | 0.911535 | 00:04 |
| 46 | 0.493950 | 0.494582 | 0.911532 | 00:04 |
| 47 | 0.490264 | 0.494583 | 0.911529 | 00:04 |
| 48 | 0.495239 | 0.494606 | 0.911524 | 00:04 |
| 49 | 0.493998 | 0.494566 | 0.911529 | 00:04 |
| 50 | 0.496160 | 0.494583 | 0.911527 | 00:04 |
Paths: 19283 , Tokens: 4508 [108, 385, 419, 1116, 2174, 2396, 2751, 2869, 3200, 3409, 3724, 3890, 4023, 4174, 4374, 4411, 4425] Adj. Rand Index Score: 0.758399. Adj. Mutual Info Score: 0.777229. Classes: [0. 1. 2. 3.] n_clusters: 4.0 Seed: 1234
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 6.567103 | 6.077250 | 0.409840 | 00:04 |
| 2 | 3.233395 | 2.826070 | 0.620577 | 00:04 |
| 3 | 1.633889 | 1.435499 | 0.779454 | 00:04 |
| 4 | 0.900862 | 0.805823 | 0.869539 | 00:04 |
| 5 | 0.635880 | 0.599523 | 0.901186 | 00:04 |
| 6 | 0.553268 | 0.535422 | 0.909263 | 00:04 |
| 7 | 0.519857 | 0.518767 | 0.910618 | 00:04 |
| 8 | 0.517749 | 0.512996 | 0.910083 | 00:04 |
| 9 | 0.512096 | 0.510819 | 0.910307 | 00:04 |
| 10 | 0.510519 | 0.510142 | 0.911039 | 00:04 |
| 11 | 0.507621 | 0.506216 | 0.911490 | 00:04 |
| 12 | 0.502458 | 0.502422 | 0.911474 | 00:04 |
| 13 | 0.507862 | 0.502164 | 0.911498 | 00:04 |
| 14 | 0.504194 | 0.500906 | 0.911524 | 00:04 |
| 15 | 0.505379 | 0.499818 | 0.911503 | 00:04 |
| 16 | 0.502710 | 0.501870 | 0.910240 | 00:04 |
| 17 | 0.498563 | 0.500947 | 0.910271 | 00:04 |
| 18 | 0.498853 | 0.500783 | 0.911421 | 00:04 |
| 19 | 0.500933 | 0.498495 | 0.911500 | 00:04 |
| 20 | 0.504128 | 0.498253 | 0.911436 | 00:04 |
| 21 | 0.502040 | 0.498109 | 0.911511 | 00:04 |
| 22 | 0.502795 | 0.500456 | 0.909546 | 00:04 |
| 23 | 0.493911 | 0.497386 | 0.911496 | 00:04 |
| 24 | 0.496243 | 0.497873 | 0.911498 | 00:04 |
| 25 | 0.499255 | 0.496821 | 0.911509 | 00:04 |
| 26 | 0.497802 | 0.496308 | 0.911509 | 00:04 |
| 27 | 0.499468 | 0.496156 | 0.911526 | 00:04 |
| 28 | 0.497907 | 0.496381 | 0.911527 | 00:04 |
| 29 | 0.498290 | 0.496757 | 0.911522 | 00:04 |
| 30 | 0.499554 | 0.496056 | 0.911530 | 00:04 |
| 31 | 0.498391 | 0.495882 | 0.911521 | 00:04 |
| 32 | 0.497052 | 0.495372 | 0.911521 | 00:04 |
| 33 | 0.497853 | 0.495620 | 0.911519 | 00:04 |
| 34 | 0.493121 | 0.495398 | 0.911532 | 00:04 |
| 35 | 0.499827 | 0.495981 | 0.910530 | 00:04 |
| 36 | 0.498413 | 0.495287 | 0.911529 | 00:04 |
| 37 | 0.497525 | 0.495444 | 0.911535 | 00:04 |
| 38 | 0.500180 | 0.495049 | 0.911537 | 00:04 |
| 39 | 0.495193 | 0.495108 | 0.911540 | 00:04 |
| 40 | 0.491059 | 0.494918 | 0.911521 | 00:04 |
| 41 | 0.495378 | 0.494890 | 0.911526 | 00:04 |
| 42 | 0.493635 | 0.494944 | 0.911540 | 00:04 |
| 43 | 0.496340 | 0.494892 | 0.911521 | 00:04 |
| 44 | 0.497452 | 0.494766 | 0.911521 | 00:04 |
| 45 | 0.490121 | 0.494665 | 0.911521 | 00:04 |
| 46 | 0.494310 | 0.494636 | 0.911532 | 00:04 |
| 47 | 0.492629 | 0.494602 | 0.911521 | 00:04 |
| 48 | 0.491547 | 0.494637 | 0.911535 | 00:04 |
| 49 | 0.493652 | 0.494587 | 0.911537 | 00:04 |
| 50 | 0.498279 | 0.494578 | 0.911530 | 00:04 |
Paths: 19283 , Tokens: 4508 [108, 385, 419, 1116, 2174, 2396, 2751, 2869, 3200, 3409, 3724, 3890, 4023, 4174, 4374, 4411, 4425] Adj. Rand Index Score: 0.758399. Adj. Mutual Info Score: 0.777229. Classes: [0. 1. 2. 3.] n_clusters: 4.0 Seed: 43210
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 6.383917 | 5.873903 | 0.460174 | 00:04 |
| 2 | 3.099858 | 2.662646 | 0.634549 | 00:04 |
| 3 | 1.569690 | 1.382594 | 0.787370 | 00:04 |
| 4 | 0.884837 | 0.791723 | 0.871468 | 00:04 |
| 5 | 0.627135 | 0.592716 | 0.901293 | 00:04 |
| 6 | 0.551325 | 0.539741 | 0.902105 | 00:04 |
| 7 | 0.521261 | 0.520157 | 0.909692 | 00:04 |
| 8 | 0.514572 | 0.523708 | 0.911399 | 00:04 |
| 9 | 0.513539 | 0.521080 | 0.911262 | 00:04 |
| 10 | 0.505717 | 0.509300 | 0.911239 | 00:04 |
| 11 | 0.510449 | 0.509232 | 0.910779 | 00:04 |
| 12 | 0.511240 | 0.506448 | 0.910454 | 00:04 |
| 13 | 0.508134 | 0.505376 | 0.911480 | 00:04 |
| 14 | 0.507567 | 0.502820 | 0.911446 | 00:04 |
| 15 | 0.502476 | 0.500838 | 0.911519 | 00:04 |
| 16 | 0.507603 | 0.501051 | 0.911498 | 00:04 |
| 17 | 0.503874 | 0.500283 | 0.911483 | 00:04 |
| 18 | 0.500898 | 0.498940 | 0.910507 | 00:04 |
| 19 | 0.503097 | 0.499592 | 0.911524 | 00:04 |
| 20 | 0.502370 | 0.500871 | 0.911503 | 00:04 |
| 21 | 0.504610 | 0.498710 | 0.911535 | 00:04 |
| 22 | 0.504093 | 0.498084 | 0.911547 | 00:04 |
| 23 | 0.502213 | 0.497456 | 0.911535 | 00:04 |
| 24 | 0.500887 | 0.496848 | 0.911529 | 00:04 |
| 25 | 0.502352 | 0.497671 | 0.911519 | 00:04 |
| 26 | 0.497999 | 0.497586 | 0.910917 | 00:04 |
| 27 | 0.500139 | 0.496353 | 0.911524 | 00:04 |
| 28 | 0.497663 | 0.497160 | 0.911475 | 00:04 |
| 29 | 0.500892 | 0.497247 | 0.911532 | 00:04 |
| 30 | 0.501002 | 0.496934 | 0.911521 | 00:04 |
| 31 | 0.501793 | 0.496621 | 0.911521 | 00:04 |
| 32 | 0.492742 | 0.496079 | 0.911532 | 00:04 |
| 33 | 0.501703 | 0.496253 | 0.911537 | 00:04 |
| 34 | 0.499625 | 0.495944 | 0.911519 | 00:04 |
| 35 | 0.503153 | 0.495715 | 0.911524 | 00:04 |
| 36 | 0.498066 | 0.495510 | 0.911532 | 00:04 |
| 37 | 0.497141 | 0.495578 | 0.911535 | 00:04 |
| 38 | 0.490588 | 0.495207 | 0.911532 | 00:04 |
| 39 | 0.502271 | 0.495390 | 0.911521 | 00:04 |
| 40 | 0.493533 | 0.495351 | 0.911537 | 00:04 |
| 41 | 0.497653 | 0.495273 | 0.911524 | 00:04 |
| 42 | 0.494762 | 0.494964 | 0.911519 | 00:04 |
| 43 | 0.492403 | 0.494861 | 0.911516 | 00:04 |
| 44 | 0.492789 | 0.494870 | 0.911524 | 00:04 |
| 45 | 0.493719 | 0.494793 | 0.911521 | 00:04 |
| 46 | 0.495700 | 0.494722 | 0.911527 | 00:04 |
| 47 | 0.489450 | 0.494644 | 0.911519 | 00:04 |
| 48 | 0.493171 | 0.494657 | 0.911521 | 00:04 |
| 49 | 0.496041 | 0.494632 | 0.911524 | 00:04 |
| 50 | 0.494040 | 0.494624 | 0.911535 | 00:04 |
Paths: 19283 , Tokens: 4508 [108, 385, 419, 1116, 2174, 2396, 2751, 2869, 3200, 3409, 3724, 3890, 4023, 4174, 4374, 4411, 4425] Adj. Rand Index Score: 0.758399. Adj. Mutual Info Score: 0.777229. Classes: [0. 1. 2. 3.] n_clusters: 4.0 Seed: 1133557799
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 6.311153 | 5.747099 | 0.447690 | 00:04 |
| 2 | 2.998718 | 2.579906 | 0.646626 | 00:04 |
| 3 | 1.520858 | 1.355617 | 0.792829 | 00:04 |
| 4 | 0.875741 | 0.788369 | 0.855256 | 00:04 |
| 5 | 0.627352 | 0.589015 | 0.893882 | 00:04 |
| 6 | 0.543193 | 0.527139 | 0.907644 | 00:04 |
| 7 | 0.524663 | 0.519561 | 0.911119 | 00:04 |
| 8 | 0.512565 | 0.513229 | 0.911443 | 00:04 |
| 9 | 0.512188 | 0.513094 | 0.902702 | 00:04 |
| 10 | 0.513148 | 0.509940 | 0.910058 | 00:04 |
| 11 | 0.512527 | 0.507598 | 0.911480 | 00:04 |
| 12 | 0.508323 | 0.508421 | 0.910707 | 00:04 |
| 13 | 0.504349 | 0.507575 | 0.908928 | 00:04 |
| 14 | 0.507052 | 0.502652 | 0.911218 | 00:04 |
| 15 | 0.506710 | 0.500758 | 0.911470 | 00:04 |
| 16 | 0.506915 | 0.500305 | 0.910504 | 00:04 |
| 17 | 0.503481 | 0.499972 | 0.911478 | 00:04 |
| 18 | 0.505662 | 0.501456 | 0.911457 | 00:04 |
| 19 | 0.503794 | 0.500600 | 0.911499 | 00:04 |
| 20 | 0.501380 | 0.498069 | 0.911503 | 00:04 |
| 21 | 0.501961 | 0.498487 | 0.911542 | 00:04 |
| 22 | 0.500599 | 0.497959 | 0.911529 | 00:04 |
| 23 | 0.503620 | 0.497950 | 0.911519 | 00:04 |
| 24 | 0.499947 | 0.499251 | 0.911537 | 00:04 |
| 25 | 0.498849 | 0.496620 | 0.911485 | 00:04 |
| 26 | 0.495410 | 0.497012 | 0.911500 | 00:04 |
| 27 | 0.498994 | 0.497425 | 0.911498 | 00:04 |
| 28 | 0.502690 | 0.497362 | 0.911548 | 00:04 |
| 29 | 0.501066 | 0.497038 | 0.911530 | 00:04 |
| 30 | 0.502619 | 0.495970 | 0.911524 | 00:04 |
| 31 | 0.498425 | 0.496229 | 0.911530 | 00:04 |
| 32 | 0.497635 | 0.495801 | 0.911516 | 00:04 |
| 33 | 0.496855 | 0.495882 | 0.911519 | 00:04 |
| 34 | 0.493527 | 0.495332 | 0.911524 | 00:04 |
| 35 | 0.497261 | 0.495437 | 0.911526 | 00:04 |
| 36 | 0.495499 | 0.495750 | 0.911527 | 00:04 |
| 37 | 0.491031 | 0.495225 | 0.911521 | 00:04 |
| 38 | 0.497696 | 0.495309 | 0.911519 | 00:04 |
| 39 | 0.496029 | 0.495138 | 0.911519 | 00:04 |
| 40 | 0.488730 | 0.495144 | 0.911524 | 00:04 |
| 41 | 0.495351 | 0.494999 | 0.911521 | 00:04 |
| 42 | 0.490797 | 0.494933 | 0.911532 | 00:04 |
| 43 | 0.495209 | 0.494900 | 0.911516 | 00:04 |
| 44 | 0.495601 | 0.494812 | 0.911530 | 00:04 |
| 45 | 0.494981 | 0.494676 | 0.911537 | 00:04 |
| 46 | 0.496601 | 0.494672 | 0.911526 | 00:04 |
| 47 | 0.496476 | 0.494642 | 0.911519 | 00:04 |
| 48 | 0.499636 | 0.494626 | 0.911524 | 00:04 |
| 49 | 0.499416 | 0.494625 | 0.911521 | 00:04 |
| 50 | 0.496098 | 0.494620 | 0.911532 | 00:04 |
Paths: 19283 , Tokens: 4508 [108, 385, 419, 1116, 2174, 2396, 2751, 2869, 3200, 3409, 3724, 3890, 4023, 4174, 4374, 4411, 4425] 0 Adj. Rand Index Score: 0.758399. Adj. Mutual Info Score: 0.777229. Classes: [0. 1. 2. 3.] n_clusters: 4.0 Seed: 22446688
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 6.511774 | 5.994586 | 0.428265 | 00:04 |
| 2 | 3.185093 | 2.750033 | 0.630586 | 00:04 |
| 3 | 1.596701 | 1.415010 | 0.783755 | 00:04 |
| 4 | 0.891688 | 0.802989 | 0.869640 | 00:04 |
| 5 | 0.636907 | 0.592670 | 0.900119 | 00:04 |
| 6 | 0.551003 | 0.530685 | 0.906452 | 00:04 |
| 7 | 0.527371 | 0.525561 | 0.903891 | 00:04 |
| 8 | 0.516031 | 0.518288 | 0.911324 | 00:04 |
| 9 | 0.515622 | 0.511869 | 0.911311 | 00:04 |
| 10 | 0.512435 | 0.510486 | 0.903945 | 00:04 |
| 11 | 0.506699 | 0.508109 | 0.911485 | 00:04 |
| 12 | 0.508414 | 0.505528 | 0.909097 | 00:04 |
| 13 | 0.506379 | 0.504274 | 0.910380 | 00:04 |
| 14 | 0.507715 | 0.504565 | 0.910499 | 00:04 |
| 15 | 0.506769 | 0.501428 | 0.911501 | 00:04 |
| 16 | 0.501007 | 0.501759 | 0.910564 | 00:04 |
| 17 | 0.500529 | 0.500942 | 0.911490 | 00:04 |
| 18 | 0.504624 | 0.498807 | 0.911524 | 00:04 |
| 19 | 0.501568 | 0.499588 | 0.911256 | 00:04 |
| 20 | 0.504967 | 0.498198 | 0.911488 | 00:04 |
| 21 | 0.500730 | 0.498571 | 0.911532 | 00:04 |
| 22 | 0.499236 | 0.498901 | 0.911545 | 00:04 |
| 23 | 0.498679 | 0.497327 | 0.911519 | 00:04 |
| 24 | 0.496717 | 0.497421 | 0.911521 | 00:04 |
| 25 | 0.503022 | 0.497200 | 0.911524 | 00:04 |
| 26 | 0.497874 | 0.497318 | 0.911542 | 00:04 |
| 27 | 0.500350 | 0.497494 | 0.911545 | 00:04 |
| 28 | 0.496128 | 0.496890 | 0.911522 | 00:04 |
| 29 | 0.500126 | 0.496784 | 0.911163 | 00:04 |
| 30 | 0.503530 | 0.496351 | 0.911537 | 00:04 |
| 31 | 0.493742 | 0.496357 | 0.911532 | 00:04 |
| 32 | 0.499944 | 0.496454 | 0.911537 | 00:04 |
| 33 | 0.500106 | 0.496229 | 0.911530 | 00:04 |
| 34 | 0.498798 | 0.496075 | 0.911516 | 00:04 |
| 35 | 0.492264 | 0.495740 | 0.911513 | 00:04 |
| 36 | 0.499939 | 0.495582 | 0.911535 | 00:04 |
| 37 | 0.488657 | 0.495423 | 0.911537 | 00:04 |
| 38 | 0.499790 | 0.495523 | 0.911516 | 00:04 |
| 39 | 0.494697 | 0.495349 | 0.911530 | 00:04 |
| 40 | 0.498539 | 0.495046 | 0.911516 | 00:04 |
| 41 | 0.496183 | 0.495187 | 0.911535 | 00:04 |
| 42 | 0.497381 | 0.495025 | 0.911519 | 00:04 |
| 43 | 0.494012 | 0.494893 | 0.911527 | 00:04 |
| 44 | 0.492214 | 0.494896 | 0.911535 | 00:04 |
| 45 | 0.493458 | 0.494823 | 0.911521 | 00:04 |
| 46 | 0.496006 | 0.494654 | 0.911529 | 00:04 |
| 47 | 0.492867 | 0.494635 | 0.911524 | 00:04 |
| 48 | 0.491724 | 0.494635 | 0.911527 | 00:04 |
| 49 | 0.490339 | 0.494609 | 0.911531 | 00:04 |
| 50 | 0.492919 | 0.494620 | 0.911529 | 00:04 |
Paths: 19283 , Tokens: 4508 [108, 385, 419, 1116, 2174, 2396, 2751, 2869, 3200, 3409, 3724, 3890, 4023, 4174, 4374, 4411, 4425] Adj. Rand Index Score: 0.758399. Adj. Mutual Info Score: 0.777229. Classes: [0. 1. 2. 3.] n_clusters: 4.0 Seed: 123456789
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 6.222045 | 5.665717 | 0.468234 | 00:04 |
| 2 | 2.952319 | 2.553141 | 0.650452 | 00:04 |
| 3 | 1.514865 | 1.351199 | 0.792338 | 00:04 |
| 4 | 0.882625 | 0.789553 | 0.871102 | 00:04 |
| 5 | 0.623838 | 0.589360 | 0.899237 | 00:04 |
| 6 | 0.541614 | 0.530220 | 0.909515 | 00:04 |
| 7 | 0.528405 | 0.523883 | 0.910423 | 00:04 |
| 8 | 0.518249 | 0.517329 | 0.910678 | 00:04 |
| 9 | 0.513210 | 0.517580 | 0.909727 | 00:04 |
| 10 | 0.516828 | 0.512543 | 0.911475 | 00:04 |
| 11 | 0.512313 | 0.509546 | 0.910992 | 00:04 |
| 12 | 0.505042 | 0.509184 | 0.910577 | 00:04 |
| 13 | 0.508389 | 0.505775 | 0.910588 | 00:04 |
| 14 | 0.511125 | 0.503854 | 0.911485 | 00:04 |
| 15 | 0.506155 | 0.500595 | 0.911519 | 00:04 |
| 16 | 0.501599 | 0.502936 | 0.911467 | 00:04 |
| 17 | 0.500959 | 0.500204 | 0.911514 | 00:04 |
| 18 | 0.501596 | 0.499840 | 0.910795 | 00:04 |
| 19 | 0.503404 | 0.498786 | 0.911514 | 00:04 |
| 20 | 0.497241 | 0.499719 | 0.911522 | 00:04 |
| 21 | 0.503550 | 0.498048 | 0.910720 | 00:04 |
| 22 | 0.501186 | 0.497609 | 0.911514 | 00:04 |
| 23 | 0.501410 | 0.498402 | 0.911472 | 00:04 |
| 24 | 0.501594 | 0.496718 | 0.911503 | 00:04 |
| 25 | 0.499830 | 0.495990 | 0.911503 | 00:04 |
| 26 | 0.497772 | 0.495761 | 0.911503 | 00:04 |
| 27 | 0.503221 | 0.495784 | 0.911511 | 00:04 |
| 28 | 0.497903 | 0.495924 | 0.911519 | 00:04 |
| 29 | 0.495152 | 0.496002 | 0.911530 | 00:04 |
| 30 | 0.503613 | 0.496427 | 0.911521 | 00:04 |
| 31 | 0.498641 | 0.495514 | 0.911530 | 00:04 |
| 32 | 0.493836 | 0.495975 | 0.911519 | 00:04 |
| 33 | 0.502300 | 0.495811 | 0.911530 | 00:04 |
| 34 | 0.500632 | 0.495052 | 0.911524 | 00:04 |
| 35 | 0.501511 | 0.495604 | 0.911516 | 00:04 |
| 36 | 0.495171 | 0.495233 | 0.911511 | 00:04 |
| 37 | 0.498390 | 0.495163 | 0.911527 | 00:04 |
| 38 | 0.493788 | 0.494944 | 0.911516 | 00:04 |
| 39 | 0.495449 | 0.495220 | 0.911519 | 00:04 |
| 40 | 0.496540 | 0.494918 | 0.911540 | 00:04 |
| 41 | 0.501158 | 0.494840 | 0.911532 | 00:04 |
| 42 | 0.495186 | 0.494740 | 0.911532 | 00:04 |
| 43 | 0.496792 | 0.494763 | 0.911524 | 00:04 |
| 44 | 0.499451 | 0.494719 | 0.911519 | 00:04 |
| 45 | 0.494483 | 0.494676 | 0.911530 | 00:04 |
| 46 | 0.494464 | 0.494625 | 0.911530 | 00:04 |
| 47 | 0.483798 | 0.494629 | 0.911543 | 00:04 |
| 48 | 0.495507 | 0.494588 | 0.911532 | 00:04 |
| 49 | 0.493002 | 0.494581 | 0.911527 | 00:04 |
| 50 | 0.497355 | 0.494604 | 0.911526 | 00:04 |
Paths: 19283 , Tokens: 4508 [108, 385, 419, 1116, 2174, 2396, 2751, 2869, 3200, 3409, 3724, 3890, 4023, 4174, 4374, 4411, 4425] 0 0 Adj. Rand Index Score: 0.758399. Adj. Mutual Info Score: 0.777229. Classes: [0. 1. 2. 3.] n_clusters: 4.0 Seed: 987654321
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 6.633595 | 6.114462 | 0.393815 | 00:04 |
| 2 | 3.344829 | 2.904063 | 0.605038 | 00:04 |
| 3 | 1.679756 | 1.483198 | 0.767543 | 00:04 |
| 4 | 0.925486 | 0.823934 | 0.870518 | 00:04 |
| 5 | 0.641597 | 0.605174 | 0.900394 | 00:04 |
| 6 | 0.554788 | 0.537200 | 0.909022 | 00:04 |
| 7 | 0.528715 | 0.524172 | 0.910930 | 00:04 |
| 8 | 0.515512 | 0.525041 | 0.905949 | 00:04 |
| 9 | 0.513710 | 0.514296 | 0.911286 | 00:04 |
| 10 | 0.515286 | 0.511148 | 0.910252 | 00:04 |
| 11 | 0.514642 | 0.513725 | 0.910756 | 00:04 |
| 12 | 0.509878 | 0.508258 | 0.910029 | 00:04 |
| 13 | 0.515179 | 0.505029 | 0.911400 | 00:04 |
| 14 | 0.504871 | 0.502847 | 0.910904 | 00:04 |
| 15 | 0.504528 | 0.501146 | 0.911501 | 00:04 |
| 16 | 0.503243 | 0.502557 | 0.911511 | 00:04 |
| 17 | 0.506470 | 0.500646 | 0.911485 | 00:04 |
| 18 | 0.501773 | 0.499622 | 0.911420 | 00:04 |
| 19 | 0.504406 | 0.497681 | 0.911506 | 00:04 |
| 20 | 0.500780 | 0.497802 | 0.911495 | 00:04 |
| 21 | 0.503332 | 0.497699 | 0.911506 | 00:04 |
| 22 | 0.506872 | 0.497552 | 0.911485 | 00:04 |
| 23 | 0.502478 | 0.496884 | 0.911501 | 00:04 |
| 24 | 0.496347 | 0.497109 | 0.911496 | 00:04 |
| 25 | 0.500915 | 0.496783 | 0.911532 | 00:04 |
| 26 | 0.505084 | 0.496379 | 0.911530 | 00:04 |
| 27 | 0.503153 | 0.496572 | 0.911506 | 00:04 |
| 28 | 0.497232 | 0.495979 | 0.911534 | 00:04 |
| 29 | 0.497594 | 0.496419 | 0.911542 | 00:04 |
| 30 | 0.498555 | 0.496111 | 0.911532 | 00:04 |
| 31 | 0.499236 | 0.495608 | 0.911521 | 00:04 |
| 32 | 0.495691 | 0.496056 | 0.911521 | 00:04 |
| 33 | 0.501407 | 0.495284 | 0.911527 | 00:04 |
| 34 | 0.498683 | 0.495567 | 0.911530 | 00:04 |
| 35 | 0.496337 | 0.495497 | 0.911524 | 00:04 |
| 36 | 0.500277 | 0.495079 | 0.911535 | 00:04 |
| 37 | 0.495755 | 0.495099 | 0.911529 | 00:04 |
| 38 | 0.497741 | 0.495193 | 0.911527 | 00:04 |
| 39 | 0.492876 | 0.494976 | 0.911540 | 00:04 |
| 40 | 0.493352 | 0.494907 | 0.911524 | 00:04 |
| 41 | 0.497051 | 0.495092 | 0.911516 | 00:04 |
| 42 | 0.497671 | 0.494935 | 0.911521 | 00:04 |
| 43 | 0.498402 | 0.494793 | 0.911513 | 00:04 |
| 44 | 0.495919 | 0.494765 | 0.911535 | 00:04 |
| 45 | 0.498595 | 0.494763 | 0.911524 | 00:04 |
| 46 | 0.493670 | 0.494688 | 0.911532 | 00:04 |
| 47 | 0.490236 | 0.494672 | 0.911519 | 00:04 |
| 48 | 0.491007 | 0.494629 | 0.911516 | 00:04 |
| 49 | 0.493050 | 0.494604 | 0.911537 | 00:04 |
| 50 | 0.494359 | 0.494629 | 0.911521 | 00:04 |
Paths: 19283 , Tokens: 4508 [108, 385, 419, 1116, 2174, 2396, 2751, 2869, 3200, 3409, 3724, 3890, 4023, 4174, 4374, 4411, 4425] Adj. Rand Index Score: 0.758399. Adj. Mutual Info Score: 0.777229. Classes: [0. 1. 2. 3.] n_clusters: 4.0 Seed: 86420
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 6.302538 | 5.741844 | 0.438258 | 00:04 |
| 2 | 2.993598 | 2.592113 | 0.643428 | 00:04 |
| 3 | 1.531483 | 1.361758 | 0.789979 | 00:04 |
| 4 | 0.875604 | 0.798746 | 0.868944 | 00:04 |
| 5 | 0.622038 | 0.590791 | 0.895299 | 00:04 |
| 6 | 0.546139 | 0.533619 | 0.906899 | 00:04 |
| 7 | 0.524033 | 0.522425 | 0.909816 | 00:04 |
| 8 | 0.521794 | 0.515130 | 0.910706 | 00:04 |
| 9 | 0.512706 | 0.515094 | 0.911259 | 00:04 |
| 10 | 0.518354 | 0.510153 | 0.911374 | 00:04 |
| 11 | 0.511431 | 0.511053 | 0.910678 | 00:04 |
| 12 | 0.513789 | 0.513643 | 0.911464 | 00:04 |
| 13 | 0.506143 | 0.505171 | 0.910982 | 00:04 |
| 14 | 0.505786 | 0.503832 | 0.911485 | 00:04 |
| 15 | 0.503751 | 0.505079 | 0.910994 | 00:04 |
| 16 | 0.505337 | 0.500077 | 0.911516 | 00:04 |
| 17 | 0.505150 | 0.500869 | 0.911459 | 00:04 |
| 18 | 0.502430 | 0.499489 | 0.911475 | 00:04 |
| 19 | 0.502984 | 0.500156 | 0.911482 | 00:04 |
| 20 | 0.497248 | 0.498815 | 0.911532 | 00:04 |
| 21 | 0.502799 | 0.497870 | 0.911524 | 00:04 |
| 22 | 0.495194 | 0.498696 | 0.911511 | 00:04 |
| 23 | 0.499395 | 0.497675 | 0.911522 | 00:04 |
| 24 | 0.500061 | 0.500077 | 0.906694 | 00:04 |
| 25 | 0.502542 | 0.498088 | 0.910623 | 00:04 |
| 26 | 0.495832 | 0.497823 | 0.911516 | 00:04 |
| 27 | 0.500248 | 0.497109 | 0.911524 | 00:04 |
| 28 | 0.498383 | 0.497358 | 0.911522 | 00:04 |
| 29 | 0.495843 | 0.496668 | 0.911529 | 00:04 |
| 30 | 0.500051 | 0.497112 | 0.911483 | 00:04 |
| 31 | 0.499631 | 0.496208 | 0.911527 | 00:04 |
| 32 | 0.497065 | 0.496149 | 0.911532 | 00:04 |
| 33 | 0.498805 | 0.496326 | 0.911532 | 00:04 |
| 34 | 0.496551 | 0.495791 | 0.911516 | 00:04 |
| 35 | 0.498496 | 0.495633 | 0.911531 | 00:04 |
| 36 | 0.495710 | 0.496222 | 0.911516 | 00:04 |
| 37 | 0.497975 | 0.495965 | 0.911521 | 00:04 |
| 38 | 0.496003 | 0.495376 | 0.911534 | 00:04 |
| 39 | 0.492734 | 0.495478 | 0.911524 | 00:04 |
| 40 | 0.497143 | 0.495411 | 0.911535 | 00:04 |
| 41 | 0.499048 | 0.495068 | 0.911519 | 00:04 |
| 42 | 0.493998 | 0.494930 | 0.911522 | 00:04 |
| 43 | 0.495525 | 0.494851 | 0.911516 | 00:04 |
| 44 | 0.494352 | 0.494836 | 0.911535 | 00:04 |
| 45 | 0.492804 | 0.494738 | 0.911530 | 00:04 |
| 46 | 0.494526 | 0.494700 | 0.911535 | 00:04 |
| 47 | 0.491948 | 0.494647 | 0.911521 | 00:04 |
| 48 | 0.494768 | 0.494656 | 0.911521 | 00:04 |
| 49 | 0.489734 | 0.494631 | 0.911526 | 00:04 |
| 50 | 0.492633 | 0.494621 | 0.911526 | 00:04 |
Paths: 19283 , Tokens: 4508 [108, 385, 419, 1116, 2174, 2396, 2751, 2869, 3200, 3409, 3724, 3890, 4023, 4174, 4374, 4411, 4425] Adj. Rand Index Score: 0.758399. Adj. Mutual Info Score: 0.777229. Classes: [0. 1. 2. 3.] n_clusters: 4.0 File Name to Train: data_sets/02_bunch/page-blocks/page-blocks.csv ############################################################# HDBSCAN ############################################################# Adj. Rand Index Score: 0.577030. Adj. Mutual Info Score: 0.354158. Classes: [-1 0 1] n_clusters: 2 QSHIFTPP ############################################################# Adj. Rand Index Score: 0.027815. Adj. Mutual Info Score: 0.089215. Classes: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18] n_clusters: 19 HQSHIFT ############################################################# Adj. Rand Index Score: 0.577030. Adj. Mutual Info Score: 0.354158. Classes: [-1 0 1] n_clusters: 2 Seed: 0
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 3.686644 | 3.363606 | 0.563756 | 00:10 |
| 2 | 1.346486 | 1.223939 | 0.821244 | 00:09 |
| 3 | 0.638392 | 0.600439 | 0.905686 | 00:09 |
| 4 | 0.473369 | 0.454292 | 0.923814 | 00:09 |
| 5 | 0.426099 | 0.429826 | 0.917858 | 00:09 |
| 6 | 0.417381 | 0.413846 | 0.925422 | 00:09 |
| 7 | 0.417179 | 0.412587 | 0.928668 | 00:09 |
| 8 | 0.411026 | 0.407863 | 0.930110 | 00:09 |
| 9 | 0.407079 | 0.415560 | 0.917235 | 00:09 |
| 10 | 0.411874 | 0.405438 | 0.930037 | 00:09 |
| 11 | 0.407186 | 0.406324 | 0.929464 | 00:09 |
| 12 | 0.405732 | 0.401726 | 0.930107 | 00:09 |
| 13 | 0.411143 | 0.400720 | 0.930220 | 00:09 |
| 14 | 0.407308 | 0.400978 | 0.930220 | 00:09 |
| 15 | 0.404414 | 0.401553 | 0.930228 | 00:09 |
| 16 | 0.408589 | 0.401595 | 0.930256 | 00:09 |
| 17 | 0.407007 | 0.399855 | 0.930299 | 00:09 |
| 18 | 0.405717 | 0.401294 | 0.929829 | 00:09 |
| 19 | 0.403317 | 0.398553 | 0.930272 | 00:09 |
| 20 | 0.404767 | 0.399100 | 0.930216 | 00:09 |
| 21 | 0.398168 | 0.399212 | 0.929987 | 00:09 |
| 22 | 0.401089 | 0.398242 | 0.930326 | 00:09 |
| 23 | 0.407235 | 0.400158 | 0.930300 | 00:09 |
| 24 | 0.407258 | 0.397767 | 0.930324 | 00:09 |
| 25 | 0.405954 | 0.398194 | 0.930319 | 00:09 |
| 26 | 0.399673 | 0.397791 | 0.930311 | 00:09 |
| 27 | 0.400046 | 0.398039 | 0.930325 | 00:09 |
| 28 | 0.405928 | 0.397463 | 0.930318 | 00:09 |
| 29 | 0.398773 | 0.397608 | 0.930326 | 00:09 |
| 30 | 0.397932 | 0.397188 | 0.930339 | 00:09 |
| 31 | 0.400666 | 0.397058 | 0.930326 | 00:09 |
| 32 | 0.398488 | 0.396859 | 0.930352 | 00:09 |
| 33 | 0.396110 | 0.396837 | 0.930353 | 00:09 |
| 34 | 0.397339 | 0.396588 | 0.930353 | 00:09 |
| 35 | 0.399661 | 0.396577 | 0.930353 | 00:09 |
| 36 | 0.393867 | 0.396584 | 0.930353 | 00:09 |
| 37 | 0.399414 | 0.396427 | 0.930352 | 00:09 |
| 38 | 0.401238 | 0.396704 | 0.930350 | 00:09 |
| 39 | 0.394030 | 0.396348 | 0.930353 | 00:09 |
| 40 | 0.395906 | 0.396199 | 0.930353 | 00:09 |
| 41 | 0.394254 | 0.396236 | 0.930353 | 00:09 |
| 42 | 0.398252 | 0.396147 | 0.930352 | 00:09 |
| 43 | 0.397729 | 0.396204 | 0.930353 | 00:09 |
| 44 | 0.397457 | 0.396094 | 0.930353 | 00:09 |
| 45 | 0.397565 | 0.396119 | 0.930350 | 00:09 |
| 46 | 0.396101 | 0.396055 | 0.930353 | 00:09 |
| 47 | 0.403096 | 0.395959 | 0.930355 | 00:09 |
| 48 | 0.392441 | 0.395943 | 0.930352 | 00:09 |
| 49 | 0.397227 | 0.395931 | 0.930355 | 00:09 |
| 50 | 0.393640 | 0.395953 | 0.930351 | 00:09 |
/home/can/anaconda3/envs/hqshift/lib/python3.6/site-packages/fastai/datasets.py:153: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details. with open(fpath, 'r') as yaml_file: return yaml.load(yaml_file)
Paths: 31223 , Tokens: 5473 [172, 372, 527, 1718, 1828, 2132, 2229, 2312, 2424, 2454, 2462, 2633, 3129, 3159, 3276, 3356, 3417, 3637, 3692, 4288, 4772, 5216, 5364, 5384, 5463] 0 Adj. Rand Index Score: 0.569727. Adj. Mutual Info Score: 0.300430. Classes: [-1. 0. 1.] n_clusters: 2.0 Seed: 23
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 3.581009 | 3.250535 | 0.563903 | 00:09 |
| 2 | 1.319307 | 1.209390 | 0.820916 | 00:09 |
| 3 | 0.637267 | 0.595163 | 0.905516 | 00:09 |
| 4 | 0.469070 | 0.452309 | 0.924134 | 00:09 |
| 5 | 0.430157 | 0.423031 | 0.926242 | 00:09 |
| 6 | 0.418012 | 0.411446 | 0.930007 | 00:09 |
| 7 | 0.413057 | 0.408487 | 0.926215 | 00:09 |
| 8 | 0.411068 | 0.408577 | 0.929177 | 00:09 |
| 9 | 0.408969 | 0.410382 | 0.929144 | 00:09 |
| 10 | 0.408813 | 0.401910 | 0.930273 | 00:09 |
| 11 | 0.409367 | 0.401091 | 0.930102 | 00:09 |
| 12 | 0.406928 | 0.402491 | 0.930140 | 00:09 |
| 13 | 0.405388 | 0.402041 | 0.930093 | 00:09 |
| 14 | 0.408120 | 0.399486 | 0.930227 | 00:09 |
| 15 | 0.409074 | 0.398841 | 0.930236 | 00:09 |
| 16 | 0.403646 | 0.399961 | 0.928994 | 00:09 |
| 17 | 0.404421 | 0.398947 | 0.930174 | 00:09 |
| 18 | 0.398311 | 0.399399 | 0.929462 | 00:09 |
| 19 | 0.401323 | 0.398222 | 0.930287 | 00:09 |
| 20 | 0.401982 | 0.398856 | 0.930222 | 00:10 |
| 21 | 0.401278 | 0.398304 | 0.930215 | 00:10 |
| 22 | 0.401317 | 0.398455 | 0.930247 | 00:09 |
| 23 | 0.406155 | 0.398420 | 0.930283 | 00:09 |
| 24 | 0.401173 | 0.397490 | 0.930299 | 00:10 |
| 25 | 0.401689 | 0.397709 | 0.930301 | 00:10 |
| 26 | 0.398644 | 0.397662 | 0.930337 | 00:09 |
| 27 | 0.404593 | 0.397723 | 0.930327 | 00:09 |
| 28 | 0.396153 | 0.397125 | 0.930328 | 00:10 |
| 29 | 0.401734 | 0.397255 | 0.930333 | 00:10 |
| 30 | 0.399990 | 0.397460 | 0.930349 | 00:09 |
| 31 | 0.395851 | 0.396828 | 0.930352 | 00:09 |
| 32 | 0.398350 | 0.396904 | 0.930345 | 00:09 |
| 33 | 0.392483 | 0.397302 | 0.930353 | 00:09 |
| 34 | 0.396695 | 0.396433 | 0.930351 | 00:09 |
| 35 | 0.401899 | 0.396897 | 0.930353 | 00:10 |
| 36 | 0.394460 | 0.396639 | 0.930351 | 00:10 |
| 37 | 0.398426 | 0.396491 | 0.930353 | 00:10 |
| 38 | 0.395278 | 0.396405 | 0.930353 | 00:09 |
| 39 | 0.396054 | 0.396212 | 0.930355 | 00:09 |
| 40 | 0.397802 | 0.396831 | 0.930355 | 00:09 |
| 41 | 0.397342 | 0.396381 | 0.930353 | 00:09 |
| 42 | 0.394825 | 0.396093 | 0.930353 | 00:09 |
| 43 | 0.394430 | 0.396009 | 0.930353 | 00:09 |
| 44 | 0.393007 | 0.396109 | 0.930353 | 00:09 |
| 45 | 0.396442 | 0.395979 | 0.930357 | 00:09 |
| 46 | 0.393430 | 0.395946 | 0.930355 | 00:09 |
| 47 | 0.397666 | 0.395946 | 0.930355 | 00:09 |
| 48 | 0.390603 | 0.395927 | 0.930353 | 00:09 |
| 49 | 0.392032 | 0.395919 | 0.930354 | 00:09 |
| 50 | 0.395708 | 0.395914 | 0.930352 | 00:09 |
Paths: 31223 , Tokens: 5473 [172, 372, 527, 1718, 1828, 2132, 2229, 2312, 2424, 2454, 2462, 2633, 3129, 3159, 3276, 3356, 3417, 3637, 3692, 4288, 4772, 5216, 5364, 5384, 5463] 0 Adj. Rand Index Score: 0.569727. Adj. Mutual Info Score: 0.300430. Classes: [-1. 0. 1.] n_clusters: 2.0 Seed: 42
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 3.747041 | 3.405118 | 0.538893 | 00:09 |
| 2 | 1.345351 | 1.257935 | 0.815496 | 00:09 |
| 3 | 0.632773 | 0.601827 | 0.903744 | 00:09 |
| 4 | 0.466711 | 0.458633 | 0.922742 | 00:09 |
| 5 | 0.425034 | 0.425849 | 0.928829 | 00:09 |
| 6 | 0.419015 | 0.415767 | 0.929088 | 00:09 |
| 7 | 0.411803 | 0.412442 | 0.928913 | 00:09 |
| 8 | 0.405127 | 0.411244 | 0.928953 | 00:09 |
| 9 | 0.409721 | 0.408889 | 0.930179 | 00:09 |
| 10 | 0.406655 | 0.404699 | 0.930185 | 00:09 |
| 11 | 0.409478 | 0.406254 | 0.928992 | 00:09 |
| 12 | 0.406055 | 0.401967 | 0.930203 | 00:09 |
| 13 | 0.407355 | 0.403592 | 0.930157 | 00:09 |
| 14 | 0.405571 | 0.400504 | 0.930172 | 00:09 |
| 15 | 0.405317 | 0.400892 | 0.930259 | 00:09 |
| 16 | 0.403486 | 0.400841 | 0.929857 | 00:09 |
| 17 | 0.397982 | 0.400714 | 0.930311 | 00:09 |
| 18 | 0.400553 | 0.401089 | 0.930243 | 00:09 |
| 19 | 0.402967 | 0.400583 | 0.930300 | 00:09 |
| 20 | 0.404325 | 0.399072 | 0.930318 | 00:09 |
| 21 | 0.408692 | 0.400002 | 0.930267 | 00:09 |
| 22 | 0.400300 | 0.399134 | 0.930323 | 00:09 |
| 23 | 0.404786 | 0.398896 | 0.929409 | 00:09 |
| 24 | 0.408506 | 0.398851 | 0.930297 | 00:09 |
| 25 | 0.401556 | 0.398864 | 0.930063 | 00:09 |
| 26 | 0.398147 | 0.398634 | 0.930261 | 00:09 |
| 27 | 0.398104 | 0.398608 | 0.930329 | 00:09 |
| 28 | 0.401491 | 0.398112 | 0.930289 | 00:09 |
| 29 | 0.403126 | 0.397894 | 0.930342 | 00:09 |
| 30 | 0.399383 | 0.397610 | 0.930208 | 00:09 |
| 31 | 0.397273 | 0.397958 | 0.930345 | 00:09 |
| 32 | 0.404442 | 0.397784 | 0.930014 | 00:09 |
| 33 | 0.399758 | 0.397757 | 0.930347 | 00:09 |
| 34 | 0.398927 | 0.397531 | 0.930331 | 00:09 |
| 35 | 0.395823 | 0.397142 | 0.930345 | 00:09 |
| 36 | 0.396322 | 0.397031 | 0.930345 | 00:09 |
| 37 | 0.399595 | 0.397273 | 0.930352 | 00:09 |
| 38 | 0.398240 | 0.396812 | 0.930346 | 00:09 |
| 39 | 0.399894 | 0.396661 | 0.930346 | 00:09 |
| 40 | 0.393927 | 0.396846 | 0.930343 | 00:09 |
| 41 | 0.398072 | 0.396492 | 0.930344 | 00:09 |
| 42 | 0.397349 | 0.396330 | 0.930348 | 00:09 |
| 43 | 0.392435 | 0.396209 | 0.930347 | 00:09 |
| 44 | 0.393428 | 0.396266 | 0.930352 | 00:09 |
| 45 | 0.393112 | 0.396055 | 0.930349 | 00:09 |
| 46 | 0.394403 | 0.396057 | 0.930353 | 00:09 |
| 47 | 0.396006 | 0.396049 | 0.930348 | 00:09 |
| 48 | 0.396428 | 0.395984 | 0.930349 | 00:09 |
| 49 | 0.392737 | 0.396032 | 0.930349 | 00:09 |
| 50 | 0.390766 | 0.395970 | 0.930355 | 00:09 |
Paths: 31223 , Tokens: 5473 [172, 372, 527, 1718, 1828, 2132, 2229, 2312, 2424, 2454, 2462, 2633, 3129, 3159, 3276, 3356, 3417, 3637, 3692, 4288, 4772, 5216, 5364, 5384, 5463] 0 Adj. Rand Index Score: 0.569727. Adj. Mutual Info Score: 0.300430. Classes: [-1. 0. 1.] n_clusters: 2.0 Seed: 1234
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 3.955576 | 3.543739 | 0.541438 | 00:09 |
| 2 | 1.375864 | 1.268803 | 0.813403 | 00:09 |
| 3 | 0.657414 | 0.606933 | 0.903389 | 00:09 |
| 4 | 0.470893 | 0.461481 | 0.924125 | 00:09 |
| 5 | 0.428709 | 0.421482 | 0.928017 | 00:09 |
| 6 | 0.412500 | 0.413970 | 0.929547 | 00:09 |
| 7 | 0.416637 | 0.408018 | 0.928998 | 00:09 |
| 8 | 0.410530 | 0.408382 | 0.930247 | 00:09 |
| 9 | 0.409068 | 0.406541 | 0.928520 | 00:09 |
| 10 | 0.404928 | 0.403036 | 0.929928 | 00:09 |
| 11 | 0.405745 | 0.403237 | 0.929478 | 00:09 |
| 12 | 0.409469 | 0.402559 | 0.930225 | 00:09 |
| 13 | 0.413968 | 0.402960 | 0.930256 | 00:09 |
| 14 | 0.407376 | 0.403238 | 0.930177 | 00:09 |
| 15 | 0.404054 | 0.401001 | 0.930276 | 00:09 |
| 16 | 0.403921 | 0.399826 | 0.930313 | 00:09 |
| 17 | 0.404355 | 0.399942 | 0.930281 | 00:09 |
| 18 | 0.409410 | 0.399009 | 0.930304 | 00:09 |
| 19 | 0.399695 | 0.399239 | 0.929474 | 00:09 |
| 20 | 0.399143 | 0.399001 | 0.930271 | 00:09 |
| 21 | 0.401163 | 0.398116 | 0.930286 | 00:09 |
| 22 | 0.409228 | 0.398133 | 0.929979 | 00:09 |
| 23 | 0.401563 | 0.397835 | 0.930309 | 00:09 |
| 24 | 0.400331 | 0.397460 | 0.930305 | 00:09 |
| 25 | 0.396957 | 0.397973 | 0.930330 | 00:09 |
| 26 | 0.401001 | 0.396982 | 0.930304 | 00:09 |
| 27 | 0.404779 | 0.397510 | 0.929574 | 00:09 |
| 28 | 0.404817 | 0.397403 | 0.930321 | 00:09 |
| 29 | 0.399288 | 0.397185 | 0.930342 | 00:09 |
| 30 | 0.397734 | 0.397119 | 0.930345 | 00:09 |
| 31 | 0.400806 | 0.397100 | 0.930328 | 00:09 |
| 32 | 0.397860 | 0.396780 | 0.930350 | 00:09 |
| 33 | 0.400571 | 0.397009 | 0.930353 | 00:09 |
| 34 | 0.395634 | 0.396790 | 0.930353 | 00:09 |
| 35 | 0.402102 | 0.396581 | 0.930353 | 00:09 |
| 36 | 0.395579 | 0.396639 | 0.930352 | 00:09 |
| 37 | 0.403282 | 0.396475 | 0.930351 | 00:09 |
| 38 | 0.400568 | 0.396519 | 0.930348 | 00:09 |
| 39 | 0.397685 | 0.396328 | 0.930353 | 00:09 |
| 40 | 0.395213 | 0.396463 | 0.930350 | 00:09 |
| 41 | 0.395856 | 0.396187 | 0.930351 | 00:09 |
| 42 | 0.392882 | 0.396166 | 0.930350 | 00:09 |
| 43 | 0.399172 | 0.396080 | 0.930352 | 00:09 |
| 44 | 0.393774 | 0.396010 | 0.930351 | 00:09 |
| 45 | 0.389217 | 0.395970 | 0.930350 | 00:09 |
| 46 | 0.398488 | 0.395966 | 0.930349 | 00:09 |
| 47 | 0.394791 | 0.395915 | 0.930353 | 00:09 |
| 48 | 0.398387 | 0.395905 | 0.930353 | 00:09 |
| 49 | 0.393246 | 0.395885 | 0.930353 | 00:09 |
| 50 | 0.389657 | 0.395916 | 0.930352 | 00:09 |
Paths: 31223 , Tokens: 5473 [172, 372, 527, 1718, 1828, 2132, 2229, 2312, 2424, 2454, 2462, 2633, 3129, 3159, 3276, 3356, 3417, 3637, 3692, 4288, 4772, 5216, 5364, 5384, 5463] Adj. Rand Index Score: 0.569727. Adj. Mutual Info Score: 0.300430. Classes: [-1. 0. 1.] n_clusters: 2.0 Seed: 43210
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 3.969830 | 3.616910 | 0.528859 | 00:09 |
| 2 | 1.415752 | 1.292803 | 0.814208 | 00:09 |
| 3 | 0.651859 | 0.614602 | 0.896366 | 00:09 |
| 4 | 0.467375 | 0.459766 | 0.923282 | 00:09 |
| 5 | 0.431857 | 0.423523 | 0.924625 | 00:09 |
| 6 | 0.418416 | 0.426220 | 0.925542 | 00:09 |
| 7 | 0.412086 | 0.411762 | 0.929347 | 00:09 |
| 8 | 0.414202 | 0.409225 | 0.928541 | 00:09 |
| 9 | 0.409847 | 0.406559 | 0.930216 | 00:09 |
| 10 | 0.409526 | 0.404408 | 0.929850 | 00:09 |
| 11 | 0.408191 | 0.403464 | 0.929811 | 00:09 |
| 12 | 0.403246 | 0.402874 | 0.930092 | 00:09 |
| 13 | 0.408494 | 0.402401 | 0.930205 | 00:09 |
| 14 | 0.404843 | 0.401821 | 0.930253 | 00:09 |
| 15 | 0.404738 | 0.399999 | 0.930242 | 00:09 |
| 16 | 0.400047 | 0.400391 | 0.929879 | 00:09 |
| 17 | 0.403781 | 0.398944 | 0.930288 | 00:09 |
| 18 | 0.403707 | 0.400968 | 0.930283 | 00:09 |
| 19 | 0.401486 | 0.399192 | 0.929928 | 00:09 |
| 20 | 0.400401 | 0.399083 | 0.930287 | 00:09 |
| 21 | 0.404893 | 0.399599 | 0.930264 | 00:09 |
| 22 | 0.403688 | 0.399645 | 0.930317 | 00:09 |
| 23 | 0.404202 | 0.398884 | 0.929644 | 00:09 |
| 24 | 0.401563 | 0.399119 | 0.930332 | 00:09 |
| 25 | 0.401432 | 0.398775 | 0.930330 | 00:10 |
| 26 | 0.400448 | 0.398259 | 0.930335 | 00:10 |
| 27 | 0.404644 | 0.398628 | 0.930337 | 00:10 |
| 28 | 0.404084 | 0.398053 | 0.930351 | 00:10 |
| 29 | 0.401928 | 0.397155 | 0.930342 | 00:10 |
| 30 | 0.399436 | 0.397639 | 0.930319 | 00:09 |
| 31 | 0.403638 | 0.398152 | 0.930348 | 00:10 |
| 32 | 0.400396 | 0.397541 | 0.930346 | 00:10 |
| 33 | 0.401625 | 0.397441 | 0.930017 | 00:10 |
| 34 | 0.398154 | 0.396943 | 0.930350 | 00:10 |
| 35 | 0.399340 | 0.396889 | 0.930347 | 00:10 |
| 36 | 0.400483 | 0.396742 | 0.930348 | 00:10 |
| 37 | 0.394507 | 0.396769 | 0.930350 | 00:10 |
| 38 | 0.396356 | 0.396853 | 0.930347 | 00:09 |
| 39 | 0.394833 | 0.396460 | 0.930350 | 00:09 |
| 40 | 0.393769 | 0.396449 | 0.930351 | 00:09 |
| 41 | 0.392062 | 0.396411 | 0.930351 | 00:09 |
| 42 | 0.395691 | 0.396372 | 0.930352 | 00:09 |
| 43 | 0.399609 | 0.396257 | 0.930349 | 00:09 |
| 44 | 0.400447 | 0.396134 | 0.930350 | 00:09 |
| 45 | 0.398186 | 0.396179 | 0.930353 | 00:09 |
| 46 | 0.393339 | 0.396103 | 0.930350 | 00:09 |
| 47 | 0.403839 | 0.396046 | 0.930353 | 00:09 |
| 48 | 0.394197 | 0.396004 | 0.930349 | 00:09 |
| 49 | 0.392453 | 0.396014 | 0.930350 | 00:09 |
| 50 | 0.396470 | 0.395998 | 0.930348 | 00:09 |
Paths: 31223 , Tokens: 5473 [172, 372, 527, 1718, 1828, 2132, 2229, 2312, 2424, 2454, 2462, 2633, 3129, 3159, 3276, 3356, 3417, 3637, 3692, 4288, 4772, 5216, 5364, 5384, 5463] Adj. Rand Index Score: 0.569727. Adj. Mutual Info Score: 0.300430. Classes: [-1. 0. 1.] n_clusters: 2.0 Seed: 1133557799
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 3.758229 | 3.451324 | 0.543794 | 00:09 |
| 2 | 1.356448 | 1.244827 | 0.816301 | 00:09 |
| 3 | 0.650908 | 0.607050 | 0.903311 | 00:09 |
| 4 | 0.476285 | 0.460758 | 0.920698 | 00:09 |
| 5 | 0.429166 | 0.422578 | 0.929007 | 00:09 |
| 6 | 0.417866 | 0.414729 | 0.927833 | 00:09 |
| 7 | 0.413264 | 0.412799 | 0.929823 | 00:09 |
| 8 | 0.408960 | 0.412702 | 0.924744 | 00:09 |
| 9 | 0.413946 | 0.407467 | 0.930207 | 00:09 |
| 10 | 0.404509 | 0.407888 | 0.928409 | 00:09 |
| 11 | 0.414432 | 0.404611 | 0.928425 | 00:09 |
| 12 | 0.403902 | 0.404222 | 0.929854 | 00:09 |
| 13 | 0.409069 | 0.403817 | 0.928266 | 00:09 |
| 14 | 0.405529 | 0.402663 | 0.930160 | 00:09 |
| 15 | 0.404397 | 0.401831 | 0.930272 | 00:09 |
| 16 | 0.406074 | 0.400647 | 0.930301 | 00:09 |
| 17 | 0.402768 | 0.400698 | 0.929909 | 00:09 |
| 18 | 0.406971 | 0.400490 | 0.930297 | 00:09 |
| 19 | 0.402641 | 0.399876 | 0.930333 | 00:09 |
| 20 | 0.398772 | 0.400484 | 0.929951 | 00:09 |
| 21 | 0.401360 | 0.399470 | 0.930329 | 00:09 |
| 22 | 0.405650 | 0.399821 | 0.930064 | 00:09 |
| 23 | 0.401064 | 0.399642 | 0.930355 | 00:09 |
| 24 | 0.396967 | 0.399789 | 0.930340 | 00:09 |
| 25 | 0.400057 | 0.398863 | 0.930351 | 00:09 |
| 26 | 0.404233 | 0.398678 | 0.930332 | 00:09 |
| 27 | 0.399324 | 0.398231 | 0.930056 | 00:09 |
| 28 | 0.397195 | 0.398073 | 0.930346 | 00:09 |
| 29 | 0.401801 | 0.398470 | 0.930347 | 00:09 |
| 30 | 0.404753 | 0.399515 | 0.929963 | 00:09 |
| 31 | 0.401645 | 0.398312 | 0.930341 | 00:09 |
| 32 | 0.403396 | 0.397705 | 0.930350 | 00:09 |
| 33 | 0.398958 | 0.398216 | 0.930345 | 00:09 |
| 34 | 0.396272 | 0.397117 | 0.930348 | 00:09 |
| 35 | 0.394135 | 0.397399 | 0.930345 | 00:09 |
| 36 | 0.400842 | 0.397611 | 0.930350 | 00:09 |
| 37 | 0.402905 | 0.397210 | 0.930353 | 00:09 |
| 38 | 0.398245 | 0.396772 | 0.930357 | 00:09 |
| 39 | 0.395253 | 0.396766 | 0.930350 | 00:09 |
| 40 | 0.396806 | 0.396573 | 0.930345 | 00:09 |
| 41 | 0.405389 | 0.396492 | 0.930353 | 00:09 |
| 42 | 0.396327 | 0.396423 | 0.930352 | 00:09 |
| 43 | 0.393731 | 0.396262 | 0.930355 | 00:09 |
| 44 | 0.400045 | 0.396202 | 0.930352 | 00:09 |
| 45 | 0.394206 | 0.396135 | 0.930352 | 00:09 |
| 46 | 0.397563 | 0.396090 | 0.930348 | 00:09 |
| 47 | 0.393073 | 0.396042 | 0.930348 | 00:09 |
| 48 | 0.396244 | 0.396006 | 0.930352 | 00:09 |
| 49 | 0.396160 | 0.396028 | 0.930352 | 00:09 |
| 50 | 0.398861 | 0.396010 | 0.930351 | 00:09 |
Paths: 31223 , Tokens: 5473 [172, 372, 527, 1718, 1828, 2132, 2229, 2312, 2424, 2454, 2462, 2633, 3129, 3159, 3276, 3356, 3417, 3637, 3692, 4288, 4772, 5216, 5364, 5384, 5463] Adj. Rand Index Score: 0.569727. Adj. Mutual Info Score: 0.300430. Classes: [-1. 0. 1.] n_clusters: 2.0 Seed: 22446688
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 3.950721 | 3.586070 | 0.533284 | 00:09 |
| 2 | 1.400505 | 1.292979 | 0.806957 | 00:09 |
| 3 | 0.644616 | 0.607404 | 0.903851 | 00:09 |
| 4 | 0.471762 | 0.460117 | 0.923063 | 00:09 |
| 5 | 0.429336 | 0.429920 | 0.920613 | 00:09 |
| 6 | 0.418526 | 0.415898 | 0.929184 | 00:09 |
| 7 | 0.407467 | 0.412339 | 0.929894 | 00:09 |
| 8 | 0.415542 | 0.408989 | 0.929180 | 00:09 |
| 9 | 0.409787 | 0.407607 | 0.925889 | 00:09 |
| 10 | 0.408631 | 0.406260 | 0.930196 | 00:09 |
| 11 | 0.407540 | 0.403992 | 0.930207 | 00:09 |
| 12 | 0.403463 | 0.401895 | 0.930237 | 00:09 |
| 13 | 0.403416 | 0.402106 | 0.930235 | 00:09 |
| 14 | 0.403207 | 0.399420 | 0.929888 | 00:09 |
| 15 | 0.403202 | 0.398353 | 0.930226 | 00:09 |
| 16 | 0.408362 | 0.398679 | 0.930262 | 00:09 |
| 17 | 0.405704 | 0.398710 | 0.930165 | 00:09 |
| 18 | 0.397833 | 0.398820 | 0.930166 | 00:09 |
| 19 | 0.399326 | 0.398184 | 0.930236 | 00:09 |
| 20 | 0.402291 | 0.398036 | 0.930215 | 00:09 |
| 21 | 0.403416 | 0.398303 | 0.930286 | 00:09 |
| 22 | 0.404276 | 0.397588 | 0.929944 | 00:09 |
| 23 | 0.402547 | 0.398258 | 0.929888 | 00:09 |
| 24 | 0.400621 | 0.397564 | 0.930327 | 00:09 |
| 25 | 0.398297 | 0.397535 | 0.929516 | 00:09 |
| 26 | 0.399137 | 0.397474 | 0.929934 | 00:09 |
| 27 | 0.401140 | 0.398190 | 0.930332 | 00:09 |
| 28 | 0.398566 | 0.397854 | 0.930343 | 00:09 |
| 29 | 0.399489 | 0.397192 | 0.930333 | 00:09 |
| 30 | 0.401829 | 0.397091 | 0.930351 | 00:09 |
| 31 | 0.402014 | 0.396850 | 0.930349 | 00:09 |
| 32 | 0.404854 | 0.396828 | 0.930349 | 00:09 |
| 33 | 0.398054 | 0.396530 | 0.930353 | 00:09 |
| 34 | 0.401072 | 0.396455 | 0.930352 | 00:09 |
| 35 | 0.393530 | 0.396674 | 0.930352 | 00:09 |
| 36 | 0.399352 | 0.396395 | 0.930352 | 00:09 |
| 37 | 0.402600 | 0.396591 | 0.930354 | 00:09 |
| 38 | 0.402396 | 0.396449 | 0.930355 | 00:09 |
| 39 | 0.397020 | 0.396363 | 0.930354 | 00:09 |
| 40 | 0.393973 | 0.396231 | 0.930355 | 00:09 |
| 41 | 0.396415 | 0.396184 | 0.930355 | 00:09 |
| 42 | 0.398730 | 0.396244 | 0.930355 | 00:09 |
| 43 | 0.394312 | 0.396242 | 0.930355 | 00:10 |
| 44 | 0.392848 | 0.396037 | 0.930354 | 00:10 |
| 45 | 0.397533 | 0.396035 | 0.930354 | 00:10 |
| 46 | 0.397655 | 0.395988 | 0.930355 | 00:10 |
| 47 | 0.398662 | 0.395930 | 0.930356 | 00:09 |
| 48 | 0.392300 | 0.395939 | 0.930354 | 00:09 |
| 49 | 0.397013 | 0.395914 | 0.930355 | 00:09 |
| 50 | 0.399568 | 0.395915 | 0.930355 | 00:09 |
Paths: 31223 , Tokens: 5473 [172, 372, 527, 1718, 1828, 2132, 2229, 2312, 2424, 2454, 2462, 2633, 3129, 3159, 3276, 3356, 3417, 3637, 3692, 4288, 4772, 5216, 5364, 5384, 5463] Adj. Rand Index Score: 0.569727. Adj. Mutual Info Score: 0.300430. Classes: [-1. 0. 1.] n_clusters: 2.0 Seed: 123456789
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 3.913286 | 3.570210 | 0.522966 | 00:09 |
| 2 | 1.416599 | 1.294890 | 0.808301 | 00:09 |
| 3 | 0.658353 | 0.611921 | 0.898794 | 00:09 |
| 4 | 0.477128 | 0.463345 | 0.918568 | 00:09 |
| 5 | 0.430998 | 0.419887 | 0.928739 | 00:09 |
| 6 | 0.421267 | 0.419643 | 0.928486 | 00:09 |
| 7 | 0.414414 | 0.409930 | 0.930174 | 00:09 |
| 8 | 0.415846 | 0.411123 | 0.929702 | 00:09 |
| 9 | 0.408587 | 0.404407 | 0.929204 | 00:09 |
| 10 | 0.413043 | 0.406583 | 0.929893 | 00:09 |
| 11 | 0.407560 | 0.402652 | 0.930266 | 00:09 |
| 12 | 0.401698 | 0.401754 | 0.929460 | 00:09 |
| 13 | 0.406592 | 0.400586 | 0.930255 | 00:09 |
| 14 | 0.405708 | 0.402277 | 0.930188 | 00:09 |
| 15 | 0.404554 | 0.400388 | 0.929917 | 00:09 |
| 16 | 0.407088 | 0.400641 | 0.929963 | 00:09 |
| 17 | 0.401854 | 0.400573 | 0.930254 | 00:09 |
| 18 | 0.403025 | 0.401318 | 0.930292 | 00:09 |
| 19 | 0.404471 | 0.398915 | 0.929995 | 00:09 |
| 20 | 0.396489 | 0.399079 | 0.930320 | 00:09 |
| 21 | 0.404421 | 0.399052 | 0.930260 | 00:09 |
| 22 | 0.407902 | 0.398580 | 0.930286 | 00:09 |
| 23 | 0.399452 | 0.398113 | 0.930309 | 00:09 |
| 24 | 0.400880 | 0.397605 | 0.930290 | 00:09 |
| 25 | 0.399469 | 0.397176 | 0.930342 | 00:09 |
| 26 | 0.404387 | 0.397249 | 0.930350 | 00:09 |
| 27 | 0.398339 | 0.397320 | 0.930321 | 00:09 |
| 28 | 0.401917 | 0.397239 | 0.930335 | 00:09 |
| 29 | 0.402540 | 0.397526 | 0.930349 | 00:09 |
| 30 | 0.397602 | 0.396979 | 0.930350 | 00:09 |
| 31 | 0.394009 | 0.396937 | 0.930323 | 00:09 |
| 32 | 0.398211 | 0.397526 | 0.930344 | 00:09 |
| 33 | 0.400237 | 0.396682 | 0.930348 | 00:09 |
| 34 | 0.401340 | 0.396932 | 0.930349 | 00:09 |
| 35 | 0.400947 | 0.397032 | 0.930353 | 00:09 |
| 36 | 0.393003 | 0.396697 | 0.930347 | 00:09 |
| 37 | 0.397889 | 0.396718 | 0.930354 | 00:09 |
| 38 | 0.400234 | 0.396711 | 0.930348 | 00:09 |
| 39 | 0.397443 | 0.396392 | 0.930351 | 00:09 |
| 40 | 0.395303 | 0.396462 | 0.930352 | 00:09 |
| 41 | 0.397605 | 0.396260 | 0.930351 | 00:09 |
| 42 | 0.397050 | 0.396086 | 0.930354 | 00:09 |
| 43 | 0.392269 | 0.396172 | 0.930349 | 00:09 |
| 44 | 0.397704 | 0.396053 | 0.930353 | 00:09 |
| 45 | 0.395013 | 0.396016 | 0.930355 | 00:09 |
| 46 | 0.385481 | 0.395987 | 0.930349 | 00:09 |
| 47 | 0.399581 | 0.395939 | 0.930354 | 00:09 |
| 48 | 0.390918 | 0.395935 | 0.930353 | 00:09 |
| 49 | 0.395118 | 0.395923 | 0.930351 | 00:09 |
| 50 | 0.398711 | 0.395948 | 0.930350 | 00:09 |
Paths: 31223 , Tokens: 5473 [172, 372, 527, 1718, 1828, 2132, 2229, 2312, 2424, 2454, 2462, 2633, 3129, 3159, 3276, 3356, 3417, 3637, 3692, 4288, 4772, 5216, 5364, 5384, 5463] 0 Adj. Rand Index Score: 0.569727. Adj. Mutual Info Score: 0.300430. Classes: [-1. 0. 1.] n_clusters: 2.0 Seed: 987654321
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 3.759575 | 3.389054 | 0.553828 | 00:10 |
| 2 | 1.352957 | 1.234150 | 0.817174 | 00:10 |
| 3 | 0.633703 | 0.600160 | 0.904786 | 00:10 |
| 4 | 0.476021 | 0.459948 | 0.921992 | 00:10 |
| 5 | 0.433674 | 0.423544 | 0.928457 | 00:09 |
| 6 | 0.410566 | 0.414769 | 0.928627 | 00:10 |
| 7 | 0.407334 | 0.411227 | 0.929358 | 00:10 |
| 8 | 0.408181 | 0.407968 | 0.929059 | 00:10 |
| 9 | 0.409319 | 0.408619 | 0.929342 | 00:10 |
| 10 | 0.405687 | 0.405440 | 0.930211 | 00:10 |
| 11 | 0.403178 | 0.405788 | 0.930193 | 00:10 |
| 12 | 0.406559 | 0.405551 | 0.929746 | 00:10 |
| 13 | 0.413343 | 0.404008 | 0.928682 | 00:09 |
| 14 | 0.405924 | 0.400498 | 0.930256 | 00:09 |
| 15 | 0.409140 | 0.401298 | 0.930239 | 00:10 |
| 16 | 0.404126 | 0.400169 | 0.930198 | 00:10 |
| 17 | 0.403506 | 0.401031 | 0.930299 | 00:09 |
| 18 | 0.407528 | 0.400481 | 0.929108 | 00:10 |
| 19 | 0.401150 | 0.398690 | 0.930330 | 00:09 |
| 20 | 0.406784 | 0.399038 | 0.930332 | 00:09 |
| 21 | 0.406142 | 0.399514 | 0.930260 | 00:10 |
| 22 | 0.401166 | 0.400083 | 0.930336 | 00:10 |
| 23 | 0.398783 | 0.399292 | 0.929962 | 00:10 |
| 24 | 0.397950 | 0.399021 | 0.929936 | 00:09 |
| 25 | 0.397807 | 0.399108 | 0.930315 | 00:09 |
| 26 | 0.400141 | 0.397758 | 0.930346 | 00:10 |
| 27 | 0.401694 | 0.398107 | 0.930336 | 00:10 |
| 28 | 0.405423 | 0.398404 | 0.929967 | 00:09 |
| 29 | 0.407516 | 0.398005 | 0.930345 | 00:10 |
| 30 | 0.398708 | 0.398263 | 0.930342 | 00:10 |
| 31 | 0.402930 | 0.398033 | 0.930338 | 00:10 |
| 32 | 0.399294 | 0.397509 | 0.930342 | 00:10 |
| 33 | 0.398898 | 0.397511 | 0.930348 | 00:10 |
| 34 | 0.397666 | 0.397507 | 0.930344 | 00:09 |
| 35 | 0.401958 | 0.397000 | 0.930342 | 00:09 |
| 36 | 0.398930 | 0.396909 | 0.930346 | 00:09 |
| 37 | 0.399595 | 0.397107 | 0.930339 | 00:09 |
| 38 | 0.394634 | 0.396802 | 0.930350 | 00:09 |
| 39 | 0.396435 | 0.396742 | 0.930349 | 00:09 |
| 40 | 0.396221 | 0.396630 | 0.930352 | 00:09 |
| 41 | 0.395905 | 0.396587 | 0.930347 | 00:09 |
| 42 | 0.393728 | 0.396590 | 0.930347 | 00:09 |
| 43 | 0.398644 | 0.396219 | 0.930343 | 00:09 |
| 44 | 0.394760 | 0.396202 | 0.930347 | 00:09 |
| 45 | 0.391934 | 0.396160 | 0.930348 | 00:09 |
| 46 | 0.394949 | 0.396115 | 0.930348 | 00:09 |
| 47 | 0.397093 | 0.396080 | 0.930347 | 00:09 |
| 48 | 0.396542 | 0.396011 | 0.930352 | 00:09 |
| 49 | 0.394607 | 0.396006 | 0.930349 | 00:09 |
| 50 | 0.394683 | 0.395999 | 0.930350 | 00:09 |
Paths: 31223 , Tokens: 5473 [172, 372, 527, 1718, 1828, 2132, 2229, 2312, 2424, 2454, 2462, 2633, 3129, 3159, 3276, 3356, 3417, 3637, 3692, 4288, 4772, 5216, 5364, 5384, 5463] Adj. Rand Index Score: 0.569727. Adj. Mutual Info Score: 0.300430. Classes: [-1. 0. 1.] n_clusters: 2.0 Seed: 86420
| epoch | train_loss | valid_loss | accuracy | time |
|---|---|---|---|---|
| 1 | 4.146927 | 3.775892 | 0.502135 | 00:10 |
| 2 | 1.494770 | 1.353839 | 0.803985 | 00:09 |
| 3 | 0.638290 | 0.620994 | 0.896225 | 00:09 |
| 4 | 0.481302 | 0.461624 | 0.918550 | 00:09 |
| 5 | 0.431629 | 0.425730 | 0.928360 | 00:09 |
| 6 | 0.414731 | 0.418303 | 0.926090 | 00:09 |
| 7 | 0.415296 | 0.412551 | 0.927827 | 00:09 |
| 8 | 0.418562 | 0.408718 | 0.929322 | 00:09 |
| 9 | 0.412157 | 0.405847 | 0.930274 | 00:09 |
| 10 | 0.407669 | 0.407050 | 0.929999 | 00:09 |
| 11 | 0.410317 | 0.403627 | 0.930247 | 00:09 |
| 12 | 0.405673 | 0.403685 | 0.929896 | 00:09 |
| 13 | 0.407737 | 0.402763 | 0.929990 | 00:09 |
| 14 | 0.405613 | 0.402600 | 0.930111 | 00:09 |
| 15 | 0.401270 | 0.401451 | 0.929488 | 00:09 |
| 16 | 0.406464 | 0.401668 | 0.929865 | 00:09 |
| 17 | 0.403041 | 0.400119 | 0.930245 | 00:09 |
| 18 | 0.403965 | 0.399970 | 0.929900 | 00:09 |
| 19 | 0.407917 | 0.400582 | 0.930299 | 00:09 |
| 20 | 0.399261 | 0.399309 | 0.930315 | 00:09 |
| 21 | 0.400014 | 0.398915 | 0.930324 | 00:09 |
| 22 | 0.401977 | 0.399152 | 0.930340 | 00:09 |
| 23 | 0.404666 | 0.399027 | 0.930326 | 00:09 |
| 24 | 0.399983 | 0.398548 | 0.930323 | 00:09 |
| 25 | 0.396824 | 0.398832 | 0.930334 | 00:09 |
| 26 | 0.398344 | 0.398383 | 0.930326 | 00:09 |
| 27 | 0.403762 | 0.398199 | 0.930347 | 00:09 |
| 28 | 0.399212 | 0.397682 | 0.930336 | 00:09 |
| 29 | 0.402679 | 0.398606 | 0.929155 | 00:10 |
| 30 | 0.397036 | 0.399839 | 0.930348 | 00:10 |
| 31 | 0.398979 | 0.397741 | 0.930338 | 00:10 |
| 32 | 0.401586 | 0.397670 | 0.930346 | 00:10 |
| 33 | 0.401921 | 0.397491 | 0.930014 | 00:10 |
| 34 | 0.400832 | 0.397191 | 0.930041 | 00:10 |
| 35 | 0.396891 | 0.397476 | 0.930342 | 00:10 |
| 36 | 0.393959 | 0.397704 | 0.930343 | 00:09 |
| 37 | 0.399579 | 0.397164 | 0.930348 | 00:09 |
| 38 | 0.395272 | 0.397066 | 0.930346 | 00:09 |
| 39 | 0.398992 | 0.396885 | 0.930343 | 00:09 |
| 40 | 0.393460 | 0.396516 | 0.930352 | 00:09 |
| 41 | 0.394705 | 0.396534 | 0.930349 | 00:09 |
| 42 | 0.396734 | 0.396414 | 0.930350 | 00:09 |
| 43 | 0.393185 | 0.396388 | 0.930347 | 00:09 |
| 44 | 0.397647 | 0.396267 | 0.930347 | 00:09 |
| 45 | 0.395430 | 0.396138 | 0.930351 | 00:09 |
| 46 | 0.394379 | 0.396059 | 0.930350 | 00:09 |
| 47 | 0.399740 | 0.396013 | 0.930351 | 00:09 |
| 48 | 0.392651 | 0.395996 | 0.930349 | 00:09 |
| 49 | 0.395876 | 0.395977 | 0.930349 | 00:09 |
| 50 | 0.392402 | 0.395965 | 0.930350 | 00:09 |
Paths: 31223 , Tokens: 5473 [172, 372, 527, 1718, 1828, 2132, 2229, 2312, 2424, 2454, 2462, 2633, 3129, 3159, 3276, 3356, 3417, 3637, 3692, 4288, 4772, 5216, 5364, 5384, 5463] Adj. Rand Index Score: 0.569727. Adj. Mutual Info Score: 0.300430. Classes: [-1. 0. 1.] n_clusters: 2.0
In [325]:
perf_output
Out[325]:
[(0.7921222771673334, 0.6256005245618719), (0.6901459877245661, 0.55538929616125), (0.9510116641908538, 0.9099986699927317), (0.9510116641908538, 0.9099986699927317), (0.9510116641908538, 0.9099986699927317), (0.9510116641908538, 0.9099986699927317), (0.9510116641908538, 0.9099986699927317), (0.9510116641908538, 0.9099986699927317), (0.9510116641908538, 0.9099986699927317), (0.9510116641908538, 0.9099986699927317), (0.9510116641908538, 0.9099986699927317), (0.9510116641908538, 0.9099986699927317), (0.2833139524710691, 0.40271481855442615), (0.333800696792064, 0.44100083357096465), (0.29480471723393786, 0.3941229139711741), (0.29480471723393786, 0.3941229139711741), (0.29480471723393786, 0.3941229139711741), (0.29480471723393786, 0.3941229139711741), (0.29480471723393786, 0.3941229139711741), (0.29480471723393786, 0.3941229139711741), (0.29480471723393786, 0.3941229139711741), (0.29480471723393786, 0.3941229139711741), (0.29480471723393786, 0.3941229139711741), (0.29480471723393786, 0.3941229139711741), (0.539408633653917, 0.5476909123005289), (0.5681159420289855, 0.5767707120409254), (0.5437515388376617, 0.5425149956512012), (0.5437515388376617, 0.5425149956512012), (0.5437515388376617, 0.5425149956512012), (0.5437515388376617, 0.5425149956512012), (0.5437515388376617, 0.5425149956512012), (0.5437515388376617, 0.5425149956512012), (0.5437515388376617, 0.5425149956512012), (0.5437515388376617, 0.5425149956512012), (0.5437515388376617, 0.5425149956512012), (0.5437515388376617, 0.5425149956512012), (0.7519593275210339, 0.8118059294436889), (0.8151147523125724, 0.8386258098212307), (0.7701848596617481, 0.8265364192870966), (0.7680244646562355, 0.823527835562166), (0.7680244646562355, 0.823527835562166), (0.7680244646562355, 0.823527835562166), (0.7680244646562355, 0.823527835562166), (0.7680244646562355, 0.823527835562166), (0.7680244646562355, 0.823527835562166), (0.7680244646562355, 0.823527835562166), (0.7680244646562355, 0.823527835562166), (0.7680244646562355, 0.823527835562166), (0.41377319922472133, 0.5049529936482817), (0.752283458595894, 0.7071262724512921), (0.4031366998347653, 0.4732620761924827), (0.4031366998347653, 0.4732620761924827), (0.4031366998347653, 0.4732620761924827), (0.4031366998347653, 0.4732620761924827), (0.4031366998347653, 0.4732620761924827), (0.4031366998347653, 0.4732620761924827), (0.4031366998347653, 0.4732620761924827), (0.4031366998347653, 0.4732620761924827), (0.4031366998347653, 0.4732620761924827), (0.4031366998347653, 0.4732620761924827), (0.7600921529693362, 0.7798879234563406), (0.48951689214025595, 0.5846435579639829), (0.7583992973262297, 0.7772286287188804), (0.7583992973262297, 0.7772286287188804), (0.7583992973262297, 0.7772286287188804), (0.7583992973262297, 0.7772286287188804), (0.7583992973262297, 0.7772286287188804), (0.7583992973262297, 0.7772286287188804), (0.7583992973262297, 0.7772286287188804), (0.7583992973262297, 0.7772286287188804), (0.7583992973262297, 0.7772286287188804), (0.7583992973262297, 0.7772286287188804), (0.5770295286165554, 0.35415845100703636), (0.027815165702526644, 0.08921525083964738), (0.56972685784943, 0.30043006553295304), (0.56972685784943, 0.30043006553295304), (0.56972685784943, 0.30043006553295304), (0.56972685784943, 0.30043006553295304), (0.56972685784943, 0.30043006553295304), (0.56972685784943, 0.30043006553295304), (0.56972685784943, 0.30043006553295304), (0.56972685784943, 0.30043006553295304), (0.56972685784943, 0.30043006553295304), (0.56972685784943, 0.30043006553295304)]
In [326]:
import json
with open('perf_out.txt', 'w') as filehandle:
json.dump(perf_output, filehandle)
Exploratory Data Analysis¶
Some exploratory data analysis to inspect the dimensionality reduction techniques:
In [254]:
#data0 = pd.read_csv("data_sets/00_bunch/banknote/banknote.csv", header=None)
#data0 = pd.read_csv("data_sets/00_bunch/glass/glass.csv", header=None)
#data0 = pd.read_csv("data_sets/00_bunch/iris/iris.csv", header=None)
#data0 = pd.read_csv("data_sets/00_bunch/mnist/mnist.csv", header=None)
data0 = pd.read_csv("data_sets/00_bunch/seeds/seeds.csv", header=None)
#data0 = pd.read_csv("data_sets/01_bunch/phoneme/phoneme.csv", header=None)
#data0 = pd.read_csv("data_sets/02_bunch/page-blocks/page-blocks.csv", header=None)
X = data0.iloc[:,:-1].values
y = data0.iloc[:, -1].values
X.shape, y.shape
Out[254]:
((210, 7), (210,))
In [137]:
pca_trans = PCA(n_components=4).fit(X)
clusterable_embedding = pca_trans.transform(X)
In [134]:
np.sum(pca_trans.explained_variance_ratio_)
Out[134]:
0.7719867961314373
In [255]:
import umap
clusterable_embedding = umap.UMAP(
n_neighbors=50,
min_dist=0.0,
n_components=4,
random_state=0,
).fit_transform(X)
In [256]:
#clusterable_embedding = X
plt.figure(figsize=fig_size)
plt.scatter(clusterable_embedding[:, 0], clusterable_embedding[:, 1],
c=y, s=50, cmap='jet');
In [257]:
#clusterable_embedding = X
plt.figure(figsize=fig_size)
plt.scatter(clusterable_embedding[:, 0], clusterable_embedding[:, 1],
c=y_hat, s=50, cmap='jet');
